{ "session_id": "20260524_204754_10d508", "model": "gpt-5.5", "base_url": "https://chatgpt.com/backend-api/codex", "platform": "telegram", "session_start": "2026-05-24T18:14:29.701025", "last_updated": "2026-05-24T22:21:31.291352", "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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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% — 1,342/1,375 chars]\n══════════════════════════════════════════════\n**Notes:** Mein Herr. Loyaler Partner.\n§\n**Name:** Sir\n§\n**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\n§\n**Language:** Deutsch mit englischen Fachbegriffen\n§\nKommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\n§\nKommunikationspräferenzen: **Textnachricht → Antwort per Text**\n§\nHealth advisor role: consolidate labs/clinical reports/YAZIO/daily health email/symptoms/events into dashboards/reports; lab extraction requires 4-eyes review before values are confirmed.\n§\nTelegram topic Health-Med: only medical strategy/Behçet/Major Vessel Disease/thrombosis/meds/nutrition/symptoms/labs/doctor questions; health system scripts/data models go to Health-Tech.\n§\nFinance/Grocery: no real amounts/IDs/API keys in chat; credentials via runtime files; no DB/CSV/keys/reports in Git/frontend; backups+audit; Preview→Confirm→Audit; no fake prices/buttons; OpenFIGI never show/store; never auto-book; grocery provider calls explicit/cache-first.\n§\nAutoShorts: TrueTraceShorts channels; JARVIS operates, user approves/posts manually in phase 1. Approval previews German, finals English; free/local TDD, no side effects until approved. Style: motion-led mechanism-reveal, quality > speed. No-gos: no public JARVIS/user/collab, politics, legal/moral risk, or lawbreaking.\n\nConversation started: Sunday, May 24, 2026 08:47 PM\nModel: gpt-5.5\nProvider: openai-codex", "tools": [ { "type": "function", "function": { "name": "browser_back", "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_click", "description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e5', '@e12')" } }, "required": [ "ref" ] } } }, { "type": "function", "function": { "name": "browser_console", "description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.", "parameters": { "type": "object", "properties": { "clear": { "type": "boolean", "default": false, "description": "If true, clear the message buffers after reading" }, "expression": { "type": "string", "description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'" } } } } }, { "type": "function", "function": { "name": "browser_get_images", "description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_navigate", "description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.", "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "The URL to navigate to (e.g., 'https://example.com')" } }, "required": [ "url" ] } } }, { "type": "function", "function": { "name": "browser_press", "description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "key": { "type": "string", "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')" } }, "required": [ "key" ] } } }, { "type": "function", "function": { "name": "browser_scroll", "description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "direction": { "type": "string", "enum": [ "up", "down" ], "description": "Direction to scroll" } }, "required": [ "direction" ] } } }, { "type": "function", "function": { "name": "browser_snapshot", "description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.", "parameters": { "type": "object", "properties": { "full": { "type": "boolean", "description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.", "default": false } } } } }, { "type": "function", "function": { "name": "browser_type", "description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e3')" }, "text": { "type": "string", "description": "The text to type into the field" } }, "required": [ "ref", "text" ] } } }, { "type": "function", "function": { "name": "browser_vision", "description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "What you want to know about the page visually. Be specific about what you're looking for." }, "annotate": { "type": "boolean", "default": false, "description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "clarify", "description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "The question to present to the user." }, "choices": { "type": "array", "items": { "type": "string" }, "maxItems": 4, "description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "cronjob", "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "description": "One of: create, list, update, pause, resume, remove, run" }, "job_id": { "type": "string", "description": "Required for update/pause/resume/remove/run" }, "prompt": { "type": "string", "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills." }, "schedule": { "type": "string", "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp" }, "name": { "type": "string", "description": "Optional human-friendly name" }, "repeat": { "type": "integer", "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)." }, "deliver": { "type": "string", "description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected." }, "skills": { "type": "array", "items": { "type": "string" }, "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills." }, "model": { "type": "object", "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.", "properties": { "provider": { "type": "string", "description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider." }, "model": { "type": "string", "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')" } }, "required": [ "model" ] }, "script": { "type": "string", "description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear." }, "no_agent": { "type": "boolean", "default": false, "description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content." }, "context_from": { "type": "array", "items": { "type": "string" }, "description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear." }, "enabled_toolsets": { "type": "array", "items": { "type": "string" }, "description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear." }, "workdir": { "type": "string", "description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated." } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "delegate_task", "description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.", "parameters": { "type": "object", "properties": { "goal": { "type": "string", "description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history." }, "context": { "type": "string", "description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs." }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks." }, "tasks": { "type": "array", "items": { "type": "object", "properties": { "goal": { "type": "string", "description": "Task goal" }, "context": { "type": "string", "description": "Task-specific context" }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction." }, "acp_command": { "type": "string", "description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Per-task ACP args override. Leave empty unless acp_command is set." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Per-task role override. See top-level 'role' for semantics." } }, "required": [ "goal" ] }, "description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable." }, "acp_command": { "type": "string", "description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided." } } } } }, { "type": "function", "function": { "name": "execute_code", "description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures", "parameters": { "type": "object", "properties": { "code": { "type": "string", "description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout." } }, "required": [ "code" ] } } }, { "type": "function", "function": { "name": "image_generate", "description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![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": "patch", "description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.", "parameters": { "type": "object", "properties": { "mode": { "type": "string", "enum": [ "replace", "patch" ], "description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.", "default": "replace" }, "path": { "type": "string", "description": "REQUIRED when mode='replace'. File path to edit." }, "old_string": { "type": "string", "description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness." }, "new_string": { "type": "string", "description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text." }, "replace_all": { "type": "boolean", "description": "Replace all occurrences instead of requiring a unique match (default: false)", "default": false }, "patch": { "type": "string", "description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch" } }, "required": [ "mode" ] } } }, { "type": "function", "function": { "name": "process", "description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "list", "poll", "log", "wait", "kill", "write", "submit", "close" ], "description": "Action to perform on background processes" }, "session_id": { "type": "string", "description": "Process session ID (from terminal background output). Required for all actions except 'list'." }, "data": { "type": "string", "description": "Text to send to process stdin (for 'write' and 'submit' actions)" }, "timeout": { "type": "integer", "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.", "minimum": 1 }, "offset": { "type": "integer", "description": "Line offset for 'log' action (default: last 200 lines)" }, "limit": { "type": "integer", "description": "Max lines to return for 'log' action", "minimum": 1 } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "read_file", "description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to read (absolute, relative, or ~/path)" }, "offset": { "type": "integer", "description": "Line number to start reading from (1-indexed, default: 1)", "default": 1, "minimum": 1 }, "limit": { "type": "integer", "description": "Maximum number of lines to read (default: 500, max: 2000)", "default": 500, "maximum": 2000 } }, "required": [ "path" ] } } }, { "type": "function", "function": { "name": "search_files", "description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.", "parameters": { "type": "object", "properties": { "pattern": { "type": "string", "description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search" }, "target": { "type": "string", "enum": [ "content", "files" ], "description": "'content' searches inside file contents, 'files' searches for files by name", "default": "content" }, "path": { "type": "string", "description": "Directory or file to search in (default: current working directory)", "default": "." }, "file_glob": { "type": "string", "description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)" }, "limit": { "type": "integer", "description": "Maximum number of results to return (default: 50)", "default": 50 }, "offset": { "type": "integer", "description": "Skip first N results for pagination (default: 0)", "default": 0 }, "output_mode": { "type": "string", "enum": [ "content", "files_only", "count" ], "description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file", "default": "content" }, "context": { "type": "integer", "description": "Number of context lines before and after each match (grep mode only)", "default": 0 } }, "required": [ "pattern" ] } } }, { "type": "function", "function": { "name": "send_message", "description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "send", "list" ], "description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms." }, "target": { "type": "string", "description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:' (DM), 'yuanbao:group:' (group chat)" }, "message": { "type": "string", "description": "The message text to send. To send an image or file, include MEDIA: (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment." } } } } }, { "type": "function", "function": { "name": "session_search", "description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)." }, "role_filter": { "type": "string", "description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs." }, "limit": { "type": "integer", "description": "Max sessions to summarize (default: 3, max: 5).", "default": 3 } } } } }, { "type": "function", "function": { "name": "skill_manage", "description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin `. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "create", "patch", "edit", "delete", "write_file", "remove_file" ], "description": "The action to perform." }, "name": { "type": "string", "description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file." }, "content": { "type": "string", "description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text." }, "old_string": { "type": "string", "description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness." }, "new_string": { "type": "string", "description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text." }, "replace_all": { "type": "boolean", "description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)." }, "category": { "type": "string", "description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'." }, "file_path": { "type": "string", "description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted." }, "file_content": { "type": "string", "description": "Content for the file. Required for 'write_file'." }, "absorbed_into": { "type": "string", "description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent." } }, "required": [ "action", "name" ] } } }, { "type": "function", "function": { "name": "skill_view", "description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')." }, "file_path": { "type": "string", "description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content." } }, "required": [ "name" ] } } }, { "type": "function", "function": { "name": "skills_list", "description": "List available skills (name + description). Use skill_view(name) to load full content.", "parameters": { "type": "object", "properties": { "category": { "type": "string", "description": "Optional category filter to narrow results" } } } } }, { "type": "function", "function": { "name": "terminal", "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n (1) Long-lived processes that never exit (servers, watchers).\n (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n", "parameters": { "type": "object", "properties": { "command": { "type": "string", "description": "The command to execute on the VM" }, "background": { "type": "boolean", "description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.", "default": false }, "timeout": { "type": "integer", "description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.", "minimum": 1 }, "workdir": { "type": "string", "description": "Working directory for this command (absolute path). Defaults to the session working directory." }, "pty": { "type": "boolean", "description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.", "default": false }, "notify_on_complete": { "type": "boolean", "description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.", "default": false }, "watch_patterns": { "type": "array", "items": { "type": "string" }, "description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both." } }, "required": [ "command" ] } } }, { "type": "function", "function": { "name": "text_to_speech", "description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.), not model-selected.", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated." }, "output_path": { "type": "string", "description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/.mp3" } }, "required": [ "text" ] } } }, { "type": "function", "function": { "name": "todo", "description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.", "parameters": { "type": "object", "properties": { "todos": { "type": "array", "description": "Task items to write. Omit to read current list.", "items": { "type": "object", "properties": { "id": { "type": "string", "description": "Unique item identifier" }, "content": { "type": "string", "description": "Task description" }, "status": { "type": "string", "enum": [ "pending", "in_progress", "completed", "cancelled" ], "description": "Current status" } }, "required": [ "id", "content", "status" ] } }, "merge": { "type": "boolean", "description": "true: update existing items by id, add new ones. false (default): replace the entire list.", "default": false } } } } }, { "type": "function", "function": { "name": "vision_analyze", "description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.", "parameters": { "type": "object", "properties": { "image_url": { "type": "string", "description": "Image URL (http/https), local file path, or data: URL to load." }, "question": { "type": "string", "description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image." } }, "required": [ "image_url", "question" ] } } }, { "type": "function", "function": { "name": "write_file", "description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)" }, "content": { "type": "string", "description": "Complete content to write to the file" } }, "required": [ "path", "content" ] } } } ], "message_count": 435, "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.\n\n[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 342 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---" }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## Neuer Baustein: Review Batch Renderer\n\nIch habe jetzt den direkt Telegram-tauglichen Batch-Renderer eingebaut:\n\n```text\nautoshorts/review/render.py\ntests/test_review_render.py\n```\n\n## Was neu ist\n\n### `render_review_batch_summary()`\n\nDiese Funktion rendert einen kompletten `ReviewBatch` als einen Telegram-sicheren Textblock.\n\nEr enthält:\n\n- Batch-ID\n- Summary Counts\n - `Ready`\n - `Blocked`\n - `Invalid`\n- vollständige Review Packages für ready Candidates\n- kompakte Fehlerlisten für blocked Candidates\n- kompakte Fehlerlisten für invalid Candidates\n\nBeispielstruktur:\n\n```text\n## Review Batch: batch-001\nReady: 1\nBlocked: 1\nInvalid: 1\n\n## Ready for review\n...\n\n## Blocked\n- blocked — Blocked title\n Reasons: quality gate blocked candidate\n\n## Invalid\n- invalid — Invalid title\n Reasons: hook is required\n```\n\n## Wichtiger Guardrail\n\nDer Renderer vermeidet bewusst Sprache wie:\n\n```text\npublish\npost\n```\n\nWarum? Weil Review und Publishing getrennte Phasen bleiben sollen.\n\nAlso kein versehentlicher semantischer Stolperdraht à la “approved-looking text = upload”. Das wäre genau die Sorte Automations-Dummheit, für die später Meetings erfunden werden.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Batch Renderer geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.review.render'\n```\n\n3. Minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n41 passed in 0.08s\n```\n\n## Neuer Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nCommit:\n\n```text\nb7b5270 feat: add review batch renderer\n```\n\nAktueller Verlauf:\n\n```text\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\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 die erste durchgängige interne Review-Strecke:\n\n```text\nContent Pillar\n→ CandidateDraft\n→ Candidate Factory\n→ VideoCandidate\n→ TestBatch\n→ Review Workflow\n → ready_for_review\n → blocked\n → invalid\n→ Review Batch Renderer\n→ Telegram-safe Review Text\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/cli/demo_review_batch.py\n```\n\noder besser als testbarer Core:\n\n```text\nautoshorts/demo/sample_batch.py\n```\n\nZiel:\n\n- sample AI-Life-Systems Batch erzeugen\n- 1 ready Candidate\n- 1 blocked Candidate\n- 1 invalid Candidate\n- ReviewBatch bauen\n- Telegram-Text rendern\n\nDann können wir mit einem einzigen Command einen echten Review-Output lokal erzeugen und später direkt an Telegram hängen." }, { "role": "user", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 49 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---" }, { "role": "assistant", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 9 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---", "tool_calls": [ { "id": "call_G7wTiBazSL0WplLH5s6nGuk8", "call_id": "call_G7wTiBazSL0WplLH5s6nGuk8", "response_item_id": "fc_09cfb159edda8ec8016a1323f611a8819191a629d2f45b3963", "type": "function", "function": { "name": "delegate_task", "arguments": "{\"tasks\": [{\"goal\": \"Research public evidence and expert commentary on what content formats currently perform well on TikTok/YouTube Shorts/Instagram Reels, especially trends, retention mechanics, hooks, and follower conv...[truncated]\", \"context\": \"User is German-speaking. We are building a JARVIS-operated short-form content machine; user only approves before posting. Prior constraints: approval previews German, final videos English; no auto-pos...[truncated]\", \"toolsets\": [\"web\", \"browser\", \"terminal\"]}, {\"goal\": \"Research creator case studies / public breakdowns where creators or content teams grew quickly with systematic/automated/AI-assisted short-form content. Evaluate which lessons are robust vs hype. Retu...[truncated]\", \"context\": \"User is German-speaking. We are building a JARVIS-operated content machine. Need examples like faceless channels, educational shorts, podcast clip systems, content batching, analytics loops, but avoid...[truncated]\", \"toolsets\": [\"web\", \"browser\", \"terminal\"]}, {\"goal\": \"Research psychological principles behind short-form content success: novelty, curiosity gaps, identity, emotional arousal, variable rewards, pattern interrupts, parasocial trust, repeatable formats vs...[truncated]\", \"context\": \"User asks specifically about psychology, variety, uniqueness, and conversion from viewer to follower. We need implementable principles for content strategy and quality gates in an AI-assisted content ...[truncated]\", \"toolsets\": [\"web\", \"browser\", \"terminal\"]}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[delegate_task] '' (11,463 chars result)", "tool_call_id": "call_G7wTiBazSL0WplLH5s6nGuk8" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_FBwpvwsrzLyrzZHbm6oUCPnM", "call_id": "call_FBwpvwsrzLyrzZHbm6oUCPnM", "response_item_id": "fc_00f48a171c3ec92c016a13265af5608191a268f8832e20ff54", "type": "function", "function": { "name": "execute_code", "arguments": "{\"code\": \"from hermes_tools import terminal\\nqueries = [\\n 'site:tiktok.com/business/en TikTok What\\\\'s Next 2025 report trends storytelling trust',\\n 'YouTube Culture and Trends Report 2025 Shorts creators communi...[truncated]\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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"id": "rs_00afa21e5325b1f0016a13266966d08191a038c14b89e3025e", "summary": [] } ] }, { "role": "tool", "content": "[browser_navigate] https://www.google.com/search?q=TikTok+What%27s+Next+2025+report+trends+storytelling+trust (932 chars)", "tool_call_id": "call_WIyJJKYBIgJz1nQhsRSCgBOU" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_CNS65qLMJ6LvI6iI5Ji0gno3", "call_id": "call_CNS65qLMJ6LvI6iI5Ji0gno3", "response_item_id": "fc_02342f6848fa5c5e016a132676ef6c8191ae02d3156fe474e5", "type": "function", "function": { "name": "browser_navigate", "arguments": "{\"url\":\"https://www.tiktok.com/business/en/blog/tiktok-whats-next-2025-trend-report\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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"id": "rs_02342f6848fa5c5e016a1326760f648191b2b40c88a86d7fe0", "summary": [] } ] }, { "role": "tool", "content": "{\"success\": false, \"error\": \"Navigation failed: net::ERR_HTTP_RESPONSE_CODE_FAILURE\"}", "tool_call_id": "call_CNS65qLMJ6LvI6iI5Ji0gno3" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_eTKtXI1krOMXtxdbyWrAdP5a", "call_id": "call_eTKtXI1krOMXtxdbyWrAdP5a", "response_item_id": "fc_0e3ccd38e72eeba5016a13267ed06c819184c8330127ca2ba9", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.request, urllib.parse, re\\nq='TikTok What\\\\'s Next 2025 trend report'\\nurl='https://www.bing.com/search?q='+urllib.parse.quote(q)\\nreq=urllib.request.Request(url, headers={'U...[truncated]\", \"timeout\": 30}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyZ-1AFehnSXOYfkBm5UVXmvwl-RhfDwO70zs0oZ-jKIGQPj-Z_OnVqBA4B2SXRp2p_9bDVnI9uIWD_zG-bhB1k_GvTqJb18TpLjQ8JSGWWZJGeCkH6VW1Pwpe_HctwvEZgSCw5T2LaG5-oFIg1eqCYNZq9JDFF0JImA9e2xKGYspJn5XPYeGMMFcEcAzEEKEsUhw_SHZGRAQuQPiVpjcak1xGGVdMR8RamYYKRoFu0Uu3prkORoUlH6_cFZVb8xC0kfpInjGJahgRH6uX2FmyOjR95Gg2dE07RUSnxMw_BcwIqXKrvEwWB1i2nC40G91_0nznY7NiWI-sSAt7PXNst3P3dgrb_CGnMV7uKe9yWzmvRwZpALUYwQB_oyd6XkGqCGWb36QVmh9xb7E7J282f5cCR-0ZVVYDlol7vLaxitrxR2Ql4Xo_F6jr1MxQu53lzMofJ_p2__t_uqUADRpixVP9-Tl4J51eHNe-_1BG36G0QW_9J4NaRI9urMUAW6R6s-bB11XnBrTlrKAA6KAiXlCdoDiK92JP7n-_2JiMvNHtlbAycX9cgur9m_0ihNCMaPcz3q_aKSKj8rx6KnGtlStO67vMlcm2VTSyWrxKXRLNezSVTqD9PWN98E2Ln07wvn3F9fJjCklirBtxqnruWODE1cRbIuN3io5632KLz_i_gnAtxCMj3m02WPvwQHr0AY5J0yfQYovegiaaIfqwLFYDa38aJ8O0E6E6R-bx5Kyai8E0vz_pC6fmb72NNEEvkaTDovK_ZpKaw1O1DSL-WkLVAd1dk1lpctZyMUy7CX_WLuvSlbzCEaIOVI_2XwHODTi_ipDEZnUdNnQFApPO4cMxp0Q2gUpdh7Dvlu972tv236lqnmd2nEeGmNrPqwjGdccWTe_w_aE52nV15irLAxNnvc7uar8qCuLr_YdbrpilJc7s8HESdrKEChtZw-lqGYt-bsX5o1xDGcjOyQsue0mcKQTIc7QXivocFBRIMavofudkXEkeNT0CfXSJPspmDbrE61wcY5RGYoJfO17ied-A==", "id": "rs_0e3ccd38e72eeba5016a13267db6e481918bd0c7e24629aeef", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request, urllib.parse, re\nq='TikTok What\\'s Nex...` -> exit 0, 1 lines output", "tool_call_id": "call_eTKtXI1krOMXtxdbyWrAdP5a" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_Gt96tO8ADGCRAz3rJvKgqvgA", "call_id": "call_Gt96tO8ADGCRAz3rJvKgqvgA", "response_item_id": "fc_0e6998c208574fb6016a132684dd5481919fc7c44031cdf67c", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.request, urllib.parse, re\\nq='TikTok What\\\\'s Next 2025 trend report'\\nhtml=urllib.request.urlopen(urllib.request.Request('https://www.bing.com/search?q='+urllib.parse.quote...[truncated]\", \"timeout\": 30}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyaEQkKp4VFRZB7gSPm5CyJZp27QbjcfTzK9vkH1MWZZb0q9iWPrHI4e435qVrjwB_sEVwGzMhTK4dTHpd0NdMWekOAhefz_rN_e42m5iTpDjwEaSSbgh5oGHeRkymCeWdF3vQWetTKd8CKTQW7-m0JI8MH74pW9LQ3aF3EC2C7H8iFvR1g8AMPNOoFq9CqherX2fnWY114rxNB8PI_G2IGs-fVJeR0wCNPtTSZZzElqbzanb2EX3Hez6uUMPJlKKLwTmxMuC22aC2VCAm4hV6HkdJMqUKfgq7xMQbBiiRbfSnWRDMFc1onvrYdzWfFqJFI3SPTVAHJBfjp9abj7WfT8a2KmUO6Cmk9PH2k1WFdT8yoVDkHPVuvXpxzGCjuEmqXVoZwS-h9Ehk-meENYYk0cC24SGEddfCDAPl4DTYuvxZ8kFuI087YCMW93LpH9vYnqNV1x0hYAHICA879MUHr7_W9nh1Bt4dP87ZXX92t5uveOipQCdvTJIddWZZj7pLrM4EhGHDkKV_XzD3tT-P_qDxutP1ZfVmmPzk-WbePFgneY0rUhUwJWhMCzk_A8nT9wviMApf1WhO_1_UKralg27XmzQ2VWoxz6A9csChE_5jL3JPxLFXTLNFyTqCq9KTrQscyC0VLM-G88Gt8VTHRdVdwfWvHc10wi3ioRf6vkEt94KC_HTK3w76T8gsXE2QFsofpA_E2a7Dr9dxWHVSGtHQD5JbW_k25YnFGu7_XPLCm2_4jiZQyecx8sIvblwKLJMjMIu6JUkAk6XMSrU-K2CqdHcBAskhhjlvt8E2q5pnocKHDvZ7IXUUH8VY3eQ71gRv5WvUd8IbiP7JR8YOMdtadoS09AIFvSTSbuJGcSgd3tZndwxGoAv6684YwtgIXK3C4tShEyPtIALxQXa82l32HfsO_CsGfC1RNOBfayEOcg4NInFznQ3EZja51Ak-BU", "id": "rs_0e6998c208574fb6016a13268441d4819196797709f506ce39", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request, urllib.parse, re\nq='TikTok What\\'s Nex...` -> exit 0, 1 lines output", "tool_call_id": "call_Gt96tO8ADGCRAz3rJvKgqvgA" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_KMMJSBXdeIKgOIzDINJPEJNa", "call_id": "call_KMMJSBXdeIKgOIzDINJPEJNa", "response_item_id": "fc_0cf5353cc182a781016a13268ebb8c81919fb29832b1af5c82", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.request\\nurls=[\\n'https://www.tiktok.com/business/en-US/blog/whats-next-2025-trend-report',\\n'https://www.tiktok.com/business/en/blog/whats-next-2025-trend-report',\\n'https:/...[truncated]\", \"timeout\": 60}" } } ], "finish_reason": "tool_calls", "reasoning": "**Exploring TikTok Trends**\n\nI'm considering the TikTok What's Next 2024/2025 Trend Report, which mentions concepts like brand fusion, identity osmosis, and creative bravery. It seems like a good idea to verify these points through official reports. I wonder if these trends could impact various industries. Let’s see what insights we can find from this report to understand how they might shape future creative directions!", "reasoning_content": "**Exploring TikTok Trends**\n\nI'm considering the TikTok What's Next 2024/2025 Trend Report, which mentions concepts like brand fusion, identity osmosis, and creative bravery. It seems like a good idea to verify these points through official reports. I wonder if these trends could impact various industries. Let’s see what insights we can find from this report to understand how they might shape future creative directions!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyaOIvSmxu0yPDQknm7XcWzV1v9_FVD7EMCicQdsF4b5ZrlhIs1LPsoKT0E36HXCx21nvRj8SuKPuCEpvf6XoMYX5PoiKUBVbDiuzyXj8XbQ2Bmwu3_SQkyNjAhacNUUJobApIGnUh10N1ZanJzAcWZyTJZb3fr8wofbGYfoDlg8r7xXU8pW7BN7kmw7ny_9bK7J9G264qFaDB5ikI2i5KJDz6ItS5HRbn67K6_Y_aEQyn95i79lpWgSPYFzi9mQvD2eOnY1MJo8D6LXkK4-M7uFgMjXDCOShMl2Qi4tB5ZNp_brar7LsMJgfHM9240QtkE96w-a1N28vnv0BvuIqxUMmf6-wsPaKZN6gbh_gfPJS9jAg4G9IBeGdzVoRI0TnzVOSFCsowJ6gRZGIfsR_i50AnuOEQO22oZeGMlWUe9aLo4oiJLdqET0rAc2C63HSwHpZjtffFm0nQPgh6y4SNn8VcCLkmo01xtRn0PztNi6JP7MgYqmbYdtylfnUu6iOLMvxC3q2Uv6XHNWav27QtPPAOY8mstOERtUsycFnlmkh6Dx6Tph6_MkI5MBHQnvCKMnL0pXjykoCTPn5nWKvgK-0ug_o8_Nx2qrln25C7Cn82QgTYLRl0SOAcLdhShBtQYMf397Z332s3fVLYUU0C1aJHzzS8x8afauGtBu1ag_e47oibSV-yjNSXbjXGGBs05Jr87CXWyEvx_NTt681cOoUNZzuEO0MYAen6yNIW5X8KM6WzK7O5hmsa_PwKMiTaD02Q8EOECQaqe3-J_yiFNtzIgc7dc3nB2ozSlTgVmSReB_bQSyrVy7GTRhZel67S1t8MfVhjBYz6FrftTUtVZYDx55pRli_G_m1iveT02tFIHyyS4nUBaUdjpQ_p-R9iTn8rD8HPq2qaF8Zb3wVehGpraPzdrx1EKONW5m-UTqFN1-SVspkyaGyY4wInYH7sbFLmMytQ7E_t09788x1bXqJUQQjbI8ey2DJhC_AlcO0yoAP3VshZKM30zdXTGh6SIorW67MwSPuVk5AgKq2JREMz9CFQIQCxYFNK7C1BXiprvHPvElx4ICG6kQ40KBrOhAIJVJWBMEnZ8uBjBpv0c-TSQyKPDhWJPu0XW2BdT-qP3j_exd-rFn-DClEe62EvgLF-eFTKhr7Ti_ADN3QwHMLmkzJ1CETs9TEEQtflv0CmpxMrs2Yps3OPkzZyHDa69gEfrTIrtNcs4veymGmoBPuP_iKHzDFlpEjHxjoGVGTSs-fyl-j4r4hnKeSg60kK1wHjQbWCxIMFItY1hILR__yQ==", "id": "rs_0cf5353cc182a781016a13268ab4148191a7bb569eb46a7368", "summary": [ { "type": "summary_text", "text": "**Exploring TikTok Trends**\n\nI'm considering the TikTok What's Next 2024/2025 Trend Report, which mentions concepts like brand fusion, identity osmosis, and creative bravery. It seems like a good idea to verify these points through official reports. I wonder if these trends could impact various industries. Let’s see what insights we can find from this report to understand how they might shape future creative directions!" } ] } ] }, { "role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request\nurls=[\n'https://www.tiktok.com/business...` -> exit 0, 1 lines output", "tool_call_id": "call_KMMJSBXdeIKgOIzDINJPEJNa" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_voMpS0KpKS0RT5AUQZah4O8S", "call_id": "call_voMpS0KpKS0RT5AUQZah4O8S", "response_item_id": "fc_04396614ecac776e016a13269c2a208191b0782e4a1dcf3c16", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.request,re,html\\nu='https://ads.tiktok.com/business/en-US/blog/whats-next-2025-trend-report'\\ntext=urllib.request.urlopen(urllib.request.Request(u,headers={'User-Agent':'Mo...[truncated]\", \"timeout\": 30}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyacZ8yqn4FG3F3Qtl2x8EfyFjvpgn8cDykV31y59sfeHfz4JaHzXZqNQVoqqSmnn1EPqXfU_hcOJrJH8kv-9u0YlUsLPG7bVcwk33LUE1jtUfSBRKrwn_DSkePhLtvc7693QCTl7LVFj4YKJ8zbNJ8HS9q36lL6BtJgTq6l6TX40oZXiKrmA0SvEE8iPF2LugTbyMWyKPOz-8Bf8ugxG_ahOjctyhKWarZ8y-CQKUI8ymyvPZnBlYl8ldhkHi0S4IopyNTkEVr0zbo9JQM0DcknxezMJYUaxKsvEkcDCGIPHVG1sVZSp9ZFvNeBZESEtdgM3shcW24rg-XVSbhHVdWBLPodFHS1WHIhhBzB_zBxhM87YUtAPeXFf-vQALFqtM98REmE_z04lAzfnpseAH3kjhlb5x0W7n6BSC9wyF0zibqP7nZ1M5WPcskl_1EQlHZZzRX-bqg-xENxAITnZx-0dWFpZ16DuI9VqMSXNNyIiVJFTNfO0FCJ1l1D5R-mDMZ7DOpyrucUHOJeSBTPeX-Mg5y7SYluntC5uxE5x1ECsFygvOmXwVrdZeeH32NvST6gM9PXZGPrrBhmH8KI9bGtNpHBAoYBTuxgUoBrThUzLupAScWvD3ijAmQzaqt8XaBtrl2sMh_CDRDSejE_m6Ytl7WrUbmMgpt2aGQcGBe3bxSUQSVGE8VzHEAtSJPIDt0VJiPM8O4GOuqvyN3l585r2lzjXdO5i6ml1gLTeh9NKl7lw6tqCmSC4aFVQtUGn39YUmER1FOcw2EuibXT_qVUyOvx2Yo9EqKUFcl_eFqYzE9bZcQbKoN-BjA8RXkaLGmujyThI9zczkreYnxyEL3BQSfQ2rF7MO7InBmgfU0_BIBA2SWmGP8GMFH__3H0wtxb_9KM3jB8nzo_CTBgavZzF7A7s_gWUy69ruzsGjJZGFY17DtVUnLOQDA05y1xMgkH", "id": "rs_04396614ecac776e016a13269ba3588191a636064c1fd60501", "summary": [] } ] }, { "role": "tool", "content": "{\"output\": \"KW Brand Fusion idx -1\\n\\n\\nKW Identity Osmosis idx -1\\n\\n\\nKW Creative Catalysts idx -1\\n\\n\\nKW Trust idx -1\\n\\n\\nKW community idx -1\\n\\n\\nKW trend idx -1\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_voMpS0KpKS0RT5AUQZah4O8S" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_ACxuByttXvJPEMMSalmioGYI", "call_id": "call_ACxuByttXvJPEMMSalmioGYI", "response_item_id": "fc_02b92e79aeda49c4016a1326a93818819190bd1e4d447f4240", "type": "function", "function": { "name": "browser_navigate", "arguments": "{\"url\":\"https://ads.tiktok.com/business/en-US/blog/whats-next-2025-trend-report\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyaptOyya0saXtoVjsq4qN0z6ilfmWdISMkPm0HowJNJ9O8As8_GCTta-gCKZ_yySwWBl6_tgfUZIaJkFqbHavPpHo1TlSzRRMgQoqzG0uMFoz95_Uu1fEPTrMA8hnPzCXX7b9l1AK-p3gpmDQPNrGlRmw1U6LxsZ0Oe7NZyTA6FQddITBpYINTMA-qKdQ6N0-tQ89vJgz22MV6FufRXORgS6iG3LZdFrcJ-hyDaGJkOWPZyNhTECJuTKHiiSbiNnBpI97DKYL51EAMfLvfks4Uhq7H0tBvy-7t76t0rh7iTwV9ihQqDpBT3Jpjl134j3DE9s74og4PHl26Tk7yEBbARnTiuKHfKWQHDlPGrv-86_xFvbvco4l9RKgo6uiUeQ1kc7STl7WJswvxTlBgSu24ctu6BaXX_BwO2eKr6Jhu5_Rfc00HAgmoXE-QDiDmfhTQS7O-H2oyFPCN3l_edt2ezsqzn6lqWs5Qucnzc1CL7tORxYoBSH2_G7V6u827UqlMtgrs4nRdiDKdMC6niABZ4gi-2fVEcTd6gVadABFMlc_hXinpzTkwUNIRiReR_aPzdDPmPKIV3D3zvKzibXdAe8xAgookex8W-vz_90XPNRUQ-t4yeqZljHgaj4XrkXZp48heh8jw_LRY94XB--OZ5ms3IPh6AJK_UtiCNJr-5kGsFH0Dva2gP6ZAgxVww4G-FjbpsrgnF1247VkDSbiKmi8Lk6hgbHqvQGtd9JDc4qg6E_wjVUlBq9foEp49MN3CHqObi-3BJoTJbzCMF3K85wB4X4b4BSa-jWVNMgRZND_QAsW5GqBnxhOKLD5D85lYSxsyQab7sJl9XMgdkKi9P-MZKtLZgUwWjVxdGg6AkUnCnm8zT7TpssoTxZ0ayGNYzlYj1gpxm1rVOVr8GkijxGQaOg0zKry9QXh_5FhATh1_vYHo5htC5tLzg2ICXx6bq", "id": "rs_02b92e79aeda49c4016a1326a827388191ac8715ebceea18b5", "summary": [] } ] }, { "role": "tool", "content": "{\"success\": false, \"error\": \"Navigation failed: net::ERR_HTTP_RESPONSE_CODE_FAILURE\"}", "tool_call_id": "call_ACxuByttXvJPEMMSalmioGYI" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_rDE1fgpOfXCwaI9KVktUsXFC", "call_id": "call_rDE1fgpOfXCwaI9KVktUsXFC", "response_item_id": "fc_0248ac7298672f62016a1326ad09808191a64fed61b9867e34", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.request, urllib.parse, re\\nq='YouTube culture and trends report 2025 Shorts fandom communities'\\nhtml=urllib.request.urlopen(urllib.request.Request('https://www.bing.com/se...[truncated]\", \"timeout\": 30}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyatc489YxkgQ_dkWqfNlZTMWGofb6x5GbMxEPSxNJptNoOvycHuIRZ30qqcz8pCyPsdFBsUlU8Hue0x7-sgGdPjWnL_wlASB1SHbLJ047hpxrryHxmNHpiBcwLYcyGgpYtAId0UN1rbecbAik3xzS_CD5Vcm1mBcx8MAUVyhIKRW-6UbhUJb5w_C2HC501TyDKMwsjkO8XBRLi0lskL0pc_56IvxfLgewIIWbDPYLLH4VPUYIGwisM7jcMC-LEVGfZy6gxtw7krFrA6F0DhhE41pV-s0S0AMQJfpXaXEgaEenlSe8rAzgBanKQNF-PaC6-KY9FjjJJaCzjMZ0D8OzR5ak0y6YVPLzaR5CpjGGPF10t3fMeGDEzaySgXBzbwJg7z5dlc2692K3y2fYSXsdbqbELiYahKP0ACsfn2i5ronZzE04_-9WRwrQiUzJ-Wtwy-TmOHldRwfD5aNMzGu7hRsBKeO8d3D-NeB3rokzgbIyJ8QQ9NUKI5od0or4nSAK4yiBiZkR5mGM1cAYqFX1Mqgl1ta7ULs4gRFeH8reIIY_coXGb_xs6IbN4SAVpUI0jhJ6VXaKYPpFsehtwM8ZtL9hdDEgUx3FzyGCfy6nwTPhv7bhvSWcOC1tx5EMWAHwTBwZtyG6fE0ngLaCVO1qHFNZ-_qBFYXJ4qhu9SlN-eaIizQD9I1wsWEYBvBAzLyJfEC_59nVtgO9vQxhcldW1XVRWR8KnqeGuJZVYHgC9r42QEjtEA9zTyuwpKLTSRn_SWe1nCEE4a6CsI_u6akbEj2NIcP9TdMvvw2Imkcu1R6EYwL96CjF6WPsmQQZ747891HdYz_XI-E40RCmnbq8Zp89-AxL1ba7iHwo8heN4Tit9ofqSoRVdevWc4Qd1qz5sKjeC5BE12oQOFOtwWIMWxj3Rxv5JJL7NfDrHqD7J6w2qyyBVAEyzx4RjxBcN9BdN0Obk8osFjbiO7b6U8U5EaDdUB105Zo5_1nClTudPoZ0gvzvDuL-mZd_xMSgpVqOnj43-fYBrpHA0K6MN7Ww2zz_yARkAP1oUybYlDYCdqM8Q=", "id": "rs_0248ac7298672f62016a1326ab89f88191a6da564501ce870d", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request, urllib.parse, re\nq='YouTube culture an...` -> exit 0, 1 lines output", "tool_call_id": "call_rDE1fgpOfXCwaI9KVktUsXFC" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_hvrGeC1SseDycwDkDnQrrLCn", "call_id": "call_hvrGeC1SseDycwDkDnQrrLCn", "response_item_id": "fc_02b0ce891db7bc10016a1326b469108191b875ca56139309c9", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request\\nurls=['https://www.youtube.com/trends/report/','https://www.youtube.com/trends/2024/','https://blog.youtube/culture-and-trends/']\\nfor u in urls:\\n try:\\n r=urllib.request.urlopen(urllib.request.Request(u,headers={'User-Agent':'Mozilla/5.0'}),timeout=20)\\n print(r.status,r.geturl(),u)\\n except Exception as e: print('ERR',u,e)\\nPY\",\"timeout\":30}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEya0ipLlud_p3lQWfMtzQx9UydYMWk3ttnpG61OBYEEh_ZlqX74mEAkgxeK8SN0dKrfjVjWaSsOz62UE6LzCEONA4XnZjxbQwQ9PixT4HPaEbk1HTP4R9MH5R8qmMat7pUogdg8DZvVZQ71VIIgQJ3qV28jN1g9FnQ0yYJ4JzCsq9m5AQpUrkDNguHUC34bR--NvAI4fRvH-joLnDxJOsUa1QcgEnxWeZgNwQKErvOYru4nru9c0Obuh83u5EBvmi-myy7GIEtet-e399NUjZYw_IOLyQm8K1rDi0LQj9YfYuml4LBds-_GnTkW4DWxlb5UtGv_7g6GY1U7cQW1kQKeD1BSGrF8PQ8ylt4xmytg5xzzOKsj_VOI-Q71WQ6rn08w4TMkeiKt7D0CRUEL5hJ5kbWff1XTQTITrIp2L1gYdx-rPD5FuOyfe1-OAq8fSviFQa1snlJNFNpEZN4D0d-cYZ60g4-BFwep2a-pTj0KrvgAEXvJGmQmCIBZUGApgACuZLXi-KxwdYgzW5mRzD77e9mK1LKO8ry4e1PGGQN5uskwN4NJwOp9mie_6laKOqEWYpnHVrrMybFyiASMAdCxXTGZ6FpIFEpyoINXFvpGK_4xIGrEI4ciaFp4afj-1lffDOEY86NchL5-8XjrMYsno49LyRT4HKnzDGc-kt2RiJohsuff3wX56nNix_0dYxbP5ObPlRtakuje3vf2DJWSiB_0ihxt1EZEP0vQpnF0LEcYyiq_pKmURGT_-JvK9eIaHmwOV_RU10BkCqod1x5tFphRJ3vKWaN2HgWgeuDzstZAaxo4f8EZoLqa4ZSWoox_6jmrpp-sbflQDCJg2JPAV3WqJGGVkOhLwXfNlYSRU1rv76Q7Ki1eJG3d-c80LWJl5YItCe8OTK3BqBqSmWMOP6bVv1-De9gOeMbjpp5zEzgunixxU3fQ38vFSuXaLGD8FgnZuyE81B4RhaMA-8ELG2XOepVlRlRh1Yl1h4N17-qY=", "id": "rs_02b0ce891db7bc10016a1326b3b34081918caa4802aec45b2d", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request\nurls=['https://www.youtube.com/trends/r...` -> exit 0, 1 lines output", "tool_call_id": "call_hvrGeC1SseDycwDkDnQrrLCn" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_AGXFxjh01lP7XZPsOu0GwQ72", "call_id": "call_AGXFxjh01lP7XZPsOu0GwQ72", "response_item_id": "fc_019bdf23729c5ba1016a1326bdf1488191beabdb5b1a22761f", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.request, urllib.parse, re, html\\nq='Jenny Hoyos YouTube Shorts strategy hook retention interview'\\nurl='https://duckduckgo.com/html/?q='+urllib.parse.quote(q)\\nreq=urllib.re...[truncated]\", \"timeout\": 30}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEya9lyATjmyRTn7NdMgOSmwS_vJfrSYu7LfGYH9haDtEyoyYQkbI8xwgL693qGepS3uHkpkgH_Q5ZLlPP4tqz5N99_2U6Sn4QWHtO2sjICfA3NH6KaW3i4PElRHXcLrRFmgRqlehsN5pyujW4BMcznD_WW4VXH-nVNKi8ZokOGuaZ2Wr_BhvIHi5AVQkwM4Nuo9U2SQ_bkolpRmdF1TWEv-uFpFezThNaICr12DLB2bLZzN49PDvwlb9b0pYbGMeg4_hrx6Bzooe8JK31w7oritikh45RDF43i_vqXHg21A9z6THYhh9djpiUUaACkOReKv4P_5XXFbSOVzXIRXxvVSO-_UTdV2qvYE3KPkQBYdHmH5GOIFMF5S3nI344ejug0bxmVZ--ZBJ3UhyfQud1DQqb8W4-sNwJgDVhOeAgmrUQzV_uR4ToYoMDIIeRdYWRmiYj3dVB3Qf8LNum9I6WYyJhhgnxWWB_JdrDfepp5-yST8luAXBNrJ8kbdhllDAV2yiO-ABLnnbHsYy7iUBuFbj-4qHAQ3ZVFTWDshyZF1M7CuOUIF2HfWM8H3rRyBSrF6bRflNUfdiFasm_F5eO2Oyj84juWdB0Q_sQ42AkwEo5nyPVa3yFArZXDWKA_RujevAx8FMfPTtZducCBncfKoCf-nq0HKBBeiwIL2fNNEq5MZXEpgQivlCZlF94HT8yl_ntrv2B_isKdGpEQ-kjjUDgCp3LoufWZeZpkEUg2HDrIkkf5wLpSA8ymOud0PAVcjS9hJCV_nVApTV4VJ5RV3Xv2bCRwOeRqFa2QIFL686S0-RB5j3AU6YqajYNy-J022pgt4MOiX7K92WdfEuBXnqmVDzMisl1M-_Zo1Mzokw1dKtwydTePXkzGSHuFCD_JEVDJg3Ayfo4HHkujn7CXmCzR2aGS29IFPAiNZM2_ELXM_A17kzv7nCh7tZTnzC3--CNOB1zMlO1QGCttg8W8e8wTfSqbDPQLoKgLkNHxz88meExWBgkXpcBeNvIVncAHd3OCpUNjE7KXXcUxZTaJNuKs47Eq1tzUSspe1wvwaLRhMIMRzVwAn-AO_bceIJKDd-hP7Ptks8TNRlNimOYc-xY67oUtxtgSUNxiZz540QO39BqBP3zNYnkmBwE4r4CciudKg9XPaMDkvXS0AaQ37wjGfiu9dM8gd1QIOQV2JrRl6VoDoW1RAC2arKVqTK233QZKwJXItgo8prjnc2WZR-JHplqgxH7fCCv3zZbnqfcXsZTuQSFTo8HHrD0VGewwVLwK-AdUQtz17dHphF2l2ybAFXUK-tK0Gj2QSHVOsQDY-9fJJgCZ_M4XqZ149cHNzPkE7xsOXM-dEDvrv558jwpxeguEVdVCmxZnPj6Dxu_UdswOHYDW0BkD9yzfjpdCaRwnCf_fjr2XwDMxaADALhD9jyX_LqgzUyBDBEw_GR7Z3mm_BHioS-3zEzH0Btm58BEl2ik-fvg75iceEi7s1264gSpY-oILusNaRTjuc6hpnE08LE9-Bpyu6puJo7TBUXhYaWXalmUuoJAsUJUGmsyAaMS0dUAA0e4VYWpJQgSP1n5O71vgN5oULfTAkhnwi2lwsIb6LGpme497izgTV2mkY7i_uQ5-dApV8JG8OBT-BTwpbkdkTuJQRGM3z9gPDh", "id": "rs_019bdf23729c5ba1016a1326ba9e7081919b5a5e6a3babda44", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request, urllib.parse, re, html\nq='Jenny Hoyos ...` -> exit 0, 1 lines output", "tool_call_id": "call_AGXFxjh01lP7XZPsOu0GwQ72" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_VkilgMfPcuL9zvbs6Bzzx59v", "call_id": "call_VkilgMfPcuL9zvbs6Bzzx59v", "response_item_id": "fc_0042ad94e211a98c016a1326c727f881918fb43996f2d05b51", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.request, re, html\\nurls=['https://www.marketingexamined.com/blog/jenny-hoyos-short-form-video-playbook','https://www.gillianperkins.com/blog/ep-202','https://www.opus.pro/...[truncated]\", \"timeout\": 60}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEybHP8te8S3wJM4UwGxSAk6Lm60IBFaXQhgz-3yB0NvgTakX-x2tymxPkGcXsdmAuQdSXuVO0MVIxgj4oGz9FJcdxoJoQT1u7XK6zGuYlA1gZ1Uypwh5PjI09iFFYa-Nxeh829sZZx66S470U4Ske5NPucJ1pl1NDLeg1awZu-2Cr3swgZ5SpMFA4xEOmNnd17ymWKBvhcRb_wB8-R7dvMChpU4lyk1i_aLr4Ib1V7ZNsGIE7gsEW9cRjIJLW5uHMwkBrsZse5tXCcDJjBrgHLGYhLUiYwQWxj9fEZDcDVoLaLGoL1e8a-tVO2llh0fXHt9tanbMRoqGaRmevoSxlw8d60AQUnOOseqrZ3tB8TDCW6NQ0y30YFwLI-t6wPIlEpUowfli3ZNDbVL2Wa2T7F6QDsrmJcSHYdXduTzlPmVtE8qJ_a6Lz1mciyoTJUyiuesPKrzfbRpfw1b8dRj6eHZd2Ekqy6h29eIuB_4jFMvHJAiJfPqVHMOMWaDSc7sLXXXFgVsilDIzkC7KAW9JO33qeJrmOc7hF8QMa2loxoy3N2S7bf_XG8kBERZKRvb3iAj_yHFqAyeskojpYLEVo1Dv3BOgceYOLmvtCHIc0oA0Lg7yE7I4ZuAD2A_BaPZdVn0wIg1I3wM1kqjl4uuSVRF3h_sCUvn_t_8mVsBfGQ_Cp7o-jE2Q-NQpraL0LvczYMMMzd2DbOPZ2t47_cgPHd554I1CEYgtyXo69xWp8kMEsj-CjM2f2WvRAZp1LfaA7RhTxuUE9juDVdgx-bBcWNBpLnD5zfjVPQapHTWJv6LmV9wWSZmVpDGYl4_J4NMeEMMYEUDu2wn9-YdkTzs2cfdwdctoUzbwuBM-PJBuoqZ5pAU551BZ34-znoiak9-3471EeiQg8r8406TZvVijr16C1oFWLmI3e4Z6p2PAYfkFO2Ex_e0riObKEzcMPAce9m5EmUM3Me3eWyWbw8hKIo3HyOfIQQDaQFR3oWNKvqxTVIA=", "id": "rs_0042ad94e211a98c016a1326c53f1c81918f5ae812e0bc6c4f", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request, re, html\nurls=['https://www.marketinge...` -> exit 0, 1 lines output", "tool_call_id": "call_VkilgMfPcuL9zvbs6Bzzx59v" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_soSn14vdU8ehMnVnVlFxjcO7", "call_id": "call_soSn14vdU8ehMnVnVlFxjcO7", "response_item_id": "fc_0fcb1fbaa43044f7016a1326d0908c819190d05f0d20855bfc", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request,re\\nu='https://ads.tiktok.com/business/en-US/blog/whats-next-2025-trend-report'\\npage=urllib.request.urlopen(urllib.request.Request(u,headers={'User-Agent':'Mozilla/5.0'}),timeout=20).read().decode('utf-8','ignore')\\nfor s in ['Brand Fusion','Creative Bravery','Identity Osmosis','Brand Chem','community','comments']:\\n print(s, page.find(s))\\nprint(len(page))\\nprint(page[:1000])\\nPY\",\"timeout\":30}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEybQCCc-jMH42nsQFnYmZIRldvRmpVXq8p7Y7l90NDMecZqUE2iyv7FAg1OaSHrLxBEUQfGOIdaMUjBcSuK6RJO-2p_PUa_cre6yDuI4T-_Ye97VB8n1zlVrngWhF0mIQrX0DeQdunxuSVIhDuK6GoiyaOJraeZOEvQeST7rT6oBzb38-FHrGaRDnvron-IWAYsri8yQBSWc8UNArMRaM8WmxMI9kok2O8V0COHD_Z8e4E-QkyuTL8-fqqcR3vG31euI80BH_7bAlnxMQwS3LSRYr7zXDwy-TNSFVjpniuEbhUkIdgt-gGK7L8-1xZVB-BJQPhJOroTW9XzPD15wZZDRYBQj0p6w7A-TNnzXbidx_LVDHfRZekuVx0dk-DM1kWKsdKNxKKLTlj6FhVVRJzLwHM3z1aHA1tH2K5jo2cVjB0BU082Ju__aUNjavrop9uUJbYZit2OQ_aZTdYNY33zeVK2iO_9yF6L7PXlulyycg7xmjwYysMt4VmdMimUVUk6eOwxbHH4XOAol0nNIMROuGRYYlQ_UoWeBwPjy9laMZItShhnrAmCBzj6mKPXZB7ACRc3ni-56X2AOHB1zbXaw5qhnaytS3PJYagyvEIyiaTNw2--4s6mikx7ArZx2KzxdoCrfRbhowLwSXjqCQfGJtJAklGx8kcwMS52zaseXlLjcQ-JFqBILeEIRqNyxR7wKMlw4-QUjOkx_hef2mADFSApjba7u_tAile--rNIy7kH4-KWBddaU8dtnw_SB7vfOtQXR2Prw4sJdnHTbCGrs68v0lqJxHhNfX8TNQM5l2kt1PMGdVemlTroGsHMZX6wlvn9XKYlhKjt21b-a8dL5mPy_mesMDF9tJWCPdH1FOas8sCJ3-LizNxYqB7MXdDqrZl5pdLPuJpBVrkY5dA1638eGMfrQ3baKjdI9CO159JrKEVk7pgbEiYBohZzx8jHoQ4F68YAR9E12ljUUTUbcvzH_twtb3u9kr3Z7p34SqOlrw4bTwBhmD_3-4vpZV11-03GlALFK8DXe1pAHf21N4Hc2kRZZmZ-OhfvhdJU7SUoo5rYzimj3vwo-867qIN5GzOOlNaPb_Qt3WVclJ2FgMrV7-MXa0yH6-aIaM9Ihfw4uaB3oyWNjsNbPve3Z9JcYcW3FVBGnjzMbgGBiivMu99BdetpKA9l-FPl_H4fRUhw75PyzJwREN4Ja2b4k_eJ9yapiddVX9biYY0tkff0OL_KcKbsqM-hgGyO5_tIYXKZ8wqgk4bcuYSmdke_P2FOUU3lfYC3ZSTyn5uJNhySDQD014GxLXwDks2VzCs_6edU=", "id": "rs_0fcb1fbaa43044f7016a1326ce7e888191990819921b649106", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request,re\nu='https://ads.tiktok.com/business/e...` -> exit 0, 1 lines output", "tool_call_id": "call_soSn14vdU8ehMnVnVlFxjcO7" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_dWX7waz5Zq4nC7bKG0tGeeJo", "call_id": "call_dWX7waz5Zq4nC7bKG0tGeeJo", "response_item_id": "fc_0595d78a39f20f20016a1326d7fc5c8191a43b2f00e6dbf85b", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.request,re,html,json\\nu='https://ads.tiktok.com/business/en-US/blog/whats-next-2025-trend-report'\\npage=urllib.request.urlopen(urllib.request.Request(u,headers={'User-Agent...[truncated]\", \"timeout\": 30}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request,re,html,json\nu='https://ads.tiktok.com/...` -> exit 0, 1 lines output", "tool_call_id": "call_dWX7waz5Zq4nC7bKG0tGeeJo" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_NpPnyY79qBXZcKX4MX6WwmZB", "call_id": "call_NpPnyY79qBXZcKX4MX6WwmZB", "response_item_id": "fc_08945ef432f87acb016a1326e385c081919f52a7cb4874ac34", "type": "function", "function": { "name": "search_files", "arguments": "{\"target\":\"files\",\"pattern\":\"docs/**\",\"path\":\".\",\"limit\":50}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering documentation needs**\n\nI’m thinking I might need to use external sources rather than exact details for this task. First, I should create a document. It seems like using a function like `write_file` with my synthesized research could work well. But before I proceed, I should inspect the documents directory to ensure everything is organized and ready for me to write in. That way, I can keep track of what I've done properly!", "reasoning_content": "**Considering documentation needs**\n\nI’m thinking I might need to use external sources rather than exact details for this task. First, I should create a document. It seems like using a function like `write_file` with my synthesized research could work well. But before I proceed, I should inspect the documents directory to ensure everything is organized and ready for me to write in. That way, I can keep track of what I've done properly!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEybjOloXXizDo4lEU5uuCIfYehv-ZXz_26yJMdBuQY1H4M59r4dZ7RIE2jIqWEBOwT27YZud_UaU2ZQ6KtKjlwW9RdERBrnv3hqW2_nYglcOdeGXP8fszUbtDYcqtBmenIJruZ4wwy3pYUthAm_JwXfP76V_UsN0bfSGhfg1Rh_f5WEKYddJrPV76bt-aTE3XCnEk7nhcX4LhFlsmNqGcw_hh-5PzI9g_goYJ5pyt04uOkEFh2GXhlzuL0ADebH3vEQfkyXpctEQY_GypeTdXSd0Rt0AyXmlvOALRV1jpNj04_g7F2xSM7qDg_0Niet_DjUpf6c8svKAP7Te2hQDhwACsb8uw6zXC3lNZmcoFtCvDWObsIpmarIon94xdXz66tQAyoRtAPjLRVUmbI46yuM-p-wlObYjQWUAWJTzCBT_aDcobTAhyBqON7SBDwltSsFIaRMMo5inQCN_dQGnKxT05CHaf47ioODKqwPJAIC-hLibpzbjdch_oBkrTg_E09yRv28-o1y6ONYX314pGUzAPTVtEFCeHwruMl-otrLyL2gEyoxthwTZdEaZut8x1n7ZU8ukuGphiFyrNppFNU2liaAKYj7Ekn1SA81XTihxQtbi3S-WkHuYmpd9gJKJ5RKe9knC4utS1g8zt0XxE6-B1HxL13TCquDHawWfyo-xvwV9g9SCCYT-pwCmUmLEbhfQFW1QTUMrzFvRKBjA9PKHWoKImQPTf0y9Beq8EN88TU_11Bd8RtggGImKVehf5dCiiwdXgxWHYsw1ibvVq5I-J29I1B5CUKexr_OUiwR1sJv4SbGy4IMTFh2Q8rDBU_xIhuZHFhFafAbtTYiQC1tRI2lWYtgJndYrdMEfxqsNtHLjHAoCOWu_oVI_3AoT5ZIOpJXo7U5KJB1YT1BmQqJuqgr8VYsMsAIIXXYmhu0yRi2ipPDsRfEmT-pK2gkH0JXh4SvzTqcL13BXS6i7JYYLHgRgUpj-dqrSUsX510lfI9K3tSthC26wcpgMg841C1CwTajjFuMtuORrG79UQs7Bk76M1GxpGf5Msmzn29EnoUUJsJ1BV59xxdFGUFnVT9jAupPcKoDJJSJpbbdY-n6GWpI_FB1a9Ph9SFU_-PDUCYw=", "id": "rs_08945ef432f87acb016a1326e196448191917649a1a95109bc", "summary": [ { "type": "summary_text", "text": "**Considering documentation needs**\n\nI’m thinking I might need to use external sources rather than exact details for this task. First, I should create a document. It seems like using a function like `write_file` with my synthesized research could work well. But before I proceed, I should inspect the documents directory to ensure everything is organized and ready for me to write in. That way, I can keep track of what I've done properly!" } ] } ] }, { "role": "tool", "content": "[search_files] files search for 'docs/**' in . -> 50 matches", "tool_call_id": "call_NpPnyY79qBXZcKX4MX6WwmZB" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_tNgl927RMg8iRKCfIh0qP4sr", "call_id": "call_tNgl927RMg8iRKCfIh0qP4sr", "response_item_id": "fc_018776a0b2606842016a1326ece3188191bbb519dd6fcb8aa0", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && pwd\",\"workdir\":\"/home/agent/.hermes/hermes-agent/jarvis_runtime/autoshortsbot/AutoShortsBot\",\"timeout\":30}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEybsmhZmpxVd3dp-R9rAFCi5lTdl3X1HwhAqyqxRDkv-iS5ivI_ZdQnVax5O7DjgkeXIJMW7foEvWjgRZ6DGYSZzhLe_gUbm8uvzZaja17jD3_31LrKUlygDaLBrKZV7Ww6jQxV17-kNeNjFUvmuSe9NDbHIcOZCzPo0SI2yZMlbvEYW1SNNWA1HhfqtxUd7dAZ3Lg74tzGHS1V0mvQWOdLWhMSeFTf_FRdStGMR0BDD9SiSFG8QHXB4qFMbg4c-djIzdaSitzmahd_C9RBK12Du4PnQNldR6AS9mlOSQh0uGcDqoMTK4rcW-9tTcS41ScKQ_RVMsMsLXE58ZCGOgzBgpFBpxY2ySccm3OT4wz7ylhEsmJBJ6dNBq-oavvsEFIIvOHESJcVGvrKqpVaf-2-z7Tb5u7ADhOMqkqMSam0WupfAMnB3XI1HbnjuAK_eugzOSxgC0FYRP5fEeFiveuBhrfKPy2Y2ZTNvT2EJli0lY5nswb6fvBqPEirWfChH7f5WK6F6M3ySH-Xh50F7pd5qNmGiWeQEGrpxGdEddv905W4d6alThoP0qnElgQ5gp0y1FMZDivfrrnOrleCd5MDQdxMX0av47j0kFmis7nkj5KVQeubrUtv_fq1_QbKomDv0N4tMGGaDyPOVJKUBEvgmvpUH5LqN3ZBShMeGKRme5KKyqnAkFjGOWTVAyC7Yx-j2XxroUFkmYwgF-AftafKA6VQp-HQvwTYMcasadTE_ezYzI6EMhO63EnBdNmYwZq7kh3YMPzghi5yJiUwUSPSvH0elcJ4sPwE7r98AzU_W4YE5XrExnb98OKI8n7tg2Xvc_W682htuxmMFO-hu96_cvq8QThnjyHgy4IK1sRe702t39dwvJ2gOrsEG4bUeSEtvVI_Zkpc2Vyiqa4vCPfLbO0cR1baVsyxwsIFYHjkbBZWLmTnW_0U2vJpuH2bUrO0pqRMh2solLMn80hhm8Ts5yST_k5guBX8rdfYka5TxYCwvdCZRzKjhnvRKDAv6J_RwhEl1GSm6ijo04rTMFj3zU55AqunjlebctK-jhWbVhD8=", "id": "rs_018776a0b2606842016a1326ebd89881918ac8f5d6cbbdf016", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `git status --short && pwd` -> exit 126, 163 lines output", "tool_call_id": "call_tNgl927RMg8iRKCfIh0qP4sr" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_gz4Nt43SiYBTmn8TSzvxyICx", "call_id": "call_gz4Nt43SiYBTmn8TSzvxyICx", "response_item_id": "fc_0e8f8d9b19f27786016a1326f448348191b44a2ba65996c1cc", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"pwd && python - <<'PY'\\nimport os\\nfor p in ['jarvis_runtime/autoshortsbot/AutoShortsBot','/home/agent/.hermes/hermes-agent/jarvis_runtime/autoshortsbot/AutoShortsBot','/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot']:\\n print(p, os.path.exists(p))\\nPY\",\"timeout\":30}" } } ], "finish_reason": "tool_calls", "reasoning": "**Evaluating file paths**\n\nI think I took a wrong turn with the file paths. When I searched, I found something in the directory like \"./jarvis_runtime,\" which makes me consider if it’s relative. But my terminal isn’t recognizing it. Maybe the current project context is set to \"/home/agent/.hermes/hermes-agent.\" If I'm checking the working directory, could it be that the terminal's current working directory is different? I'll check with \"pwd\" to clarify.", "reasoning_content": "**Evaluating file paths**\n\nI think I took a wrong turn with the file paths. When I searched, I found something in the directory like \"./jarvis_runtime,\" which makes me consider if it’s relative. But my terminal isn’t recognizing it. Maybe the current project context is set to \"/home/agent/.hermes/hermes-agent.\" If I'm checking the working directory, could it be that the terminal's current working directory is different? I'll check with \"pwd\" to clarify.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyb0YvDXfgV03QiSE2K8H8HPi69l0S-4mG6KFD6fZulV5M7Zn_VCDVjLV09Sf_u_RtsGM9dc1XEMdg2bR-8aqPzOMPE-TCGZNo-bZpz7abwEOIrP-TwLT_SoS_KsKUgm0-6KZDQjYvsonYYxUKPNVjia9zR3Cw2l5JM-Yt-u_vVDa25_zEYN6p4Dv3FeEeTmnh9nYLxT-iWIRHPQgYWm5Wg4h91Bibb85Ac83ik4WReLuQEn1x0RsYngFL0fd6-L1KEyP6Iv4gDKJGW_KiJzyBD5QgrCDorNCSgNbRcg1TYL4ASJd0TbWsjUK1y5JJNdBAE3J-rA1Gdc5XM-6_ktBgk6IOpBL8sj5yTIzN259X8_1qT0iVzeIuy-q8E-WuylGmguAlFwpZaLdbrRLpBUzTCIBQC7asYal-VODUJ0wCjxcQsfwQ-vpT-1Kl1eWQzJAZyi9nInOo1z0Xp-vgFH9ORNmMi6YQpSjIgcjv9eKgTbHyXjXIwFkwdAuQGncdbCO-ov3rDJMoCRiDYNS5aROo8wgGNchNj5fsZnH8Zw84ydbhAafAIgy6aVgiGpJ6ErpxzitxTHNReM9rVBXfxggl9aQcXxzKgzZiO7NYIprZODEq-Yq82WB69d--CR0SuREL-ZUK0JqNEXpoK92UFppaZljw1AYRt55UN9VReu6-7_zx00hruwBAaRNfvr_6CeLykiZpiEqzFT2xrAA4zFaKtAcWTZAHcVk1I1NzvPVlF4pDAQ5ALqo0MlfstZl-CxjqwMEjkv4k4UPaYxttTwdt2G2xQmdGaPkEEoYjl995G7-4yfkiMCKIpbYZfkOisyjXqkxeFbNJhpzj7Mysf1lIH9obVDl9GtKILkMH5k1nT0bC6dw7c58Eep9j5phP5Oog3ws3eCrYpTaqiqBa-OMKo412BdrImljtFlRSlYVpucmSI_wXVTf18rS71LRZtHp1E3-RRZS8b9KfYtkrpN2-5fPq3sTNRtoKSHBI9STo_AVd0nJQT9gDAWVjFB6RkXYeKUtQ29OZMNYs76Rhoo6izQRaOss0r9rTM3FKROBw1u66mUFTCQyGvtdSWKVoYEudQ_Lh11E8oi8tULmELovNjJrU9PN_eWYYMH98oqjIdXWbsB9tcKGR2PWr98Qvdp0nVfLISRxQYI6rZuuZjv_L9AK2Nm-PiMCScvncqgKXah0l68wLIWwOiLBnloq6qgHcXi2NwVU0CtkdhaN_rE1XeOsP-y49nVkcVGrEhEPRYRq-9p4OwXnybA3MFIzRjxv6j_Xctr2iumhgbtmmVAqQSK-4_uenjZK92eexTogKZQaX1sItFvtTaPq3bq-w79ejsD1ori0A2na8alMJ-AmR3Y3yIIbXOYz5O3e0eOND_DBXxTaxO18ciGL7q-YSecJzR6", "id": "rs_0e8f8d9b19f27786016a1326f045c88191b4da4830edc1803d", "summary": [ { "type": "summary_text", "text": "**Evaluating file paths**\n\nI think I took a wrong turn with the file paths. When I searched, I found something in the directory like \"./jarvis_runtime,\" which makes me consider if it’s relative. But my terminal isn’t recognizing it. Maybe the current project context is set to \"/home/agent/.hermes/hermes-agent.\" If I'm checking the working directory, could it be that the terminal's current working directory is different? I'll check with \"pwd\" to clarify." } ] } ] }, { "role": "tool", "content": "[terminal] ran `pwd && python - <<'PY'\nimport os\nfor p in ['jarvis_runtime/autoshortsbot/Auto...` -> exit 0, 1 lines output", "tool_call_id": "call_gz4Nt43SiYBTmn8TSzvxyICx" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_Qq4sWOC8wDCaab8607qyFwU8", "call_id": "call_Qq4sWOC8wDCaab8607qyFwU8", "response_item_id": "fc_045feb303569522d016a1326f95e008191901751c208ad5d50", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_automation_research.md\",\"limit\":1000}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyb5ficRlUFNxrIarboV453rNrGy5KkKkBYiGW-iVbnkzJEvDtatqhChK6Na07sW-tF6mc4kf0E72BhpoAsEF8KmAXvAYKIjrLyb2C_5H0tPQBxlnWyvepmqPGKYffyDpOtDyMroiOF4LMgmWZNIK9l0LHFpJb-ZJYs044qS--CjvEkMeoC522VhVwnNeFeLYW3z0Fg5U2RJP3mcIrMznOBMF_gNiRQiGPyLNOKJv59VILI_yXRK4reACPwPd1r9tGl4yAIt7_16ORuv2zPh7oFeW0T_XDR-n7QgiSTPTztfKXPqx_iefjFMt6iKoamTaBAgvEvKD-lBclYGLUqvU6-6xfo5SA4C0gniFK0O62fLdTig_SoLb51EJJaakonqAzk-8WlL7irypTfEeN-Lu6iRWLSbU-tAtesuTHrA-mYlKFwizL_jpMXMncoQDStLtrGHInrFQ09Wil7V9tvFc0rXVXS14SnEu3b7WYp7vLy72SXaKY_pu9zHTr5-h6CK_FIzlm-bZewOqyAbtngUFiaECAFd8Xe03UVDkJHaUi9W4lBLm9rx088wYqktW7jwKMGLgMJ1XULTvdgkm2WpkVoOl4U32Mnvimj9efd2gaLCSfgbzKUaI9x4LOVa4EHOCLIQihJjEslIttlmJdaNFBQpBYvVBxxJI6UBq5nCbFwNotjKUUz3Zg3jd3Q3Rk0osQve81BQodqBNx-H5Do75ohTUHqU3aK0MeCvgdZ_L4p-Bf4ERMRRAHKVRuSAJWa5CcI4GKUvGMy3gAeaFi6ER7MWyfdSmHuhh_aCS34WDz1H6w28oXmLZKAlRwWfC8N0LL7JGPlOHI-O54kk2HApyGJdjiCRBTtrZh1E4eZ5tmDt87DNoYEJLUZq7sUoGXz9cbOCV6uzcppT7z7QPMTRUh3dp8Osr-M1yLr4vHgpgCDgShyEQdOqBMT7fJVIsVq5RBzerkp-jAHOcFTk-cShiSSuY3CcsEWgOXm55JpItHw8z0I=", "id": "rs_045feb303569522d016a1326f73b4081918b8895b87d9137cc", "summary": [] } ] }, { "role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_automation_research.md from line 1 (6,456 chars)", "tool_call_id": "call_Qq4sWOC8wDCaab8607qyFwU8" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_y5wkFAvnvrmCdNB5GbWUJmfp", "call_id": "call_y5wkFAvnvrmCdNB5GbWUJmfp", "response_item_id": "fc_03ddc5f695dbd0ae016a132708fd9481918dec669f778a6f1a", "type": "function", "function": { "name": "write_file", "arguments": "{\"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_growth_strategy_research.md\", \"content\": \"# Content Growth Strategy Research Notes\\n\\nDate: 2026-05-24\\n\\n## Operating model decision\\n\\nThe content machine should be operated and maintained by JARVIS. The human role is approval before posting, plu...[truncated]\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Documenting AI Systems**\n\nI see the concept of JARVIS as an orchestrator for \"AI Life Systems\" instead of just following hype. I want to emphasize that it's about \"approval-first\" and prioritizing transparency. Also, I think it's interesting to note it should be internal in German but finalized in English. The focus should be on designing mechanisms that aren’t just generic; they need to feel tailored and specific. Now, I need to write up this document to clarify and elaborate on these ideas!", "reasoning_content": "**Documenting AI Systems**\n\nI see the concept of JARVIS as an orchestrator for \"AI Life Systems\" instead of just following hype. I want to emphasize that it's about \"approval-first\" and prioritizing transparency. Also, I think it's interesting to note it should be internal in German but finalized in English. The focus should be on designing mechanisms that aren’t just generic; they need to feel tailored and specific. Now, I need to write up this document to clarify and elaborate on these ideas!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEycI1jZVcvZOcbT_VqUh_QGJ7GlcV4QSwaGSkX06cEIjxy6M02SqUrhxJ5aw7m5QSjuQ68nkyDMZ4ucxJsF2hd8Yllx91Cw15rKrHUrNqqthLa5HdEhF_gvN0nOm4fOf1y66UhcTxViyNGotkDYV_XDdXs6Zj2Y2xzWj68XRGXl9Vsls-cmeZCydcPWpWZzeUCFKpwUV6UISrNYRIe8v7-MyRFVVyxh4lRRjpQ_yV4g-wgpCbCbl3a6qT3Pnitza8EWGdkSef_Wh6kGSdeiGh41mWb57qHDg_qbrqEkYeghk6Xdz6kl6O-CCiraDI1SMp6Rx2EbFpz3Mt2U9T_oqM900NGgiaxl8WmVaU40BiWtplVStBGfsM4W2FaT5AJapBp4ybEmO0wcnayXQDJKfEbTV8WcnECRCO0u2tIRhxPxtP1t94XWZLvBjt8kt6NaR6e9h4k4M29gGfrJttk-Iulx3yPvmH0ZTLQfAtqz15z4GkEihjhla2oLzcsxjKVzE33OcSwGaVd9xOQJA4AdyzfxP3J_CIGSrZOZYP4X4dul0hjtnioKnjooH0lswHP0O0y2sV2ByrFyFku0PwICyS66hEasOFHqfAt3_IBaPYg9QGjHg3VV267BtNdP1xFvX-HxwSGMaIqQQJ9zdsIfXGe1REG0XPTrPWeIF218rpku7gBL19GIKV7dPMioUD7ZEvj_AEM7tcbV52tSVms_oBT3nmVU8WJE9h7hBB2aN0YYdWTuXC8mDUYNR-Nwcr_EO4jWWJ9snya9aU67WAvXLsdGIduPpV8bJ_3ol6M6NEnljcWZ-yrnFXLH9MVpWb9hCs4B7H-Hsjo5ZOykIYHdwcVrDKDTMJNnZcqgku_sJgF3BRBzWU5BFyGpIc26cWNKAa4pgrg94wNpSIqbphx6D7vc8h1L14uLkmaPh9Aci0Pm23hiPV7uzpIM-3wTE4rZy5VaFIWTkELc68iCuCrTCiE3-DMlrNDTdxe8idfw-ooW9gKmf-QmjX-oEs8FQ9lLd2fPc6y9D1-q5Dw9p6-tlJBFdHW2PvWTbHmyMr2JJpDQ3Lkpbvv_pONcm4XI7WJEmG2YM4we5JMaLIBUySDnWRUaKkMCGG6IFRHiOoS-1OnrW-epB8rn5k01sFb3yRMk9AY04_4D2YY8KZxzsRPT1GyBjFZVJ8PtHUKY_R4cwraQ3ggN0ZwHKFg-qrmkMkY5SjZqXoMBzveB9ZxwCrLcqxoznanmC8sljJjRzjjQRlE7I9-2VMdUsKoNxBWA2q_QO_n9TfhOzbWSaqhBSKc2FBQk0e6uKNoAWNr-iBeB3htMKfFktyCQtJjKO3Gqef0BwK2TleQxTCyvEuJnd_KBwp_fW5n4HLe0uqk0nibYbuTCGz8a1__EVlcCXY9AQyBUnAXhUeLEryNiJEHhPm4M4Re3moR6wLX120Jd6J1JcMhp4MwMMA1JWWyyDVYkT5ytOR8-xAxxlSqscUdWGm51m2EaIKsiS1v7qIXCXYOe5Fd9rBF75e3OuzdQRq144lpKqG1s-RTuUOJbZVzsbV2o6GeSd-vHUXagzM7ApuaKBryDmyYQ560yFlaidOXQWcDakvSI7wKZK8dL8rn_bSAHo6DYSkpZs6ezHUbL7rZoZT7b4q02KNIPBC2kozK5X-MHZy6VttrA6z-37nPSHe6YjBJLMPZ8dYAavSHMeToYSFrEVuJRfhWw7MWm7WHjgUAfquYZ5jHr5EEMbfEEALjH9GAVf4SOTw1qSLX-372VzsMO3lCAkL6Tkc7hZbcgv0QExekEXtvjeh5sVFKpVqKk6Sr5Sp3VhRxuQo3CmmjgRVkq5hznVzENLBpx2U-yQ6732Fu35NsQYHsQQJI1tUYe01moNof0y868pL11-GcMi5vmrGKUUrD7BsDssR7bynlCW7fHxu8utrPFgcCzHzLl1gcmcwcrkkic5u5h8741-zgujg07Jzvqqc1gqqsasuOsnpx3UTcyA1dnPzktX5yBxk8w7aQfjEb8Y0kYDKCQ8hVNilwUeZVZdvBNVI5WeInBFjkaNC4oHIdBOKCZW2GZGKoSObKN1qG133zOaz1e51COYVHyGWRFHe74IBhOBfYnVyOxDxnTQ3_7Ktnr1VpZsyWo1dtA8Bfjl8ifblyn5fsxnSJt-W1Fl6xgimMVsffERhjXHtP-lKUMHbhNzTyyfqZXLv_cIxNDvel3AwiFI9j2qDnRAx5fMD6pr6APzbT-BRvLYXFCNoeeGJfig5xZaXKfV-vf-b1CGtZ28H5Op7v4fBKnxDe3-9wLmAA-wpdijydARmb1jru-hmeIlJgtvHtYHXU6Dy4TGOWYuRaeSTOZYZ8Tr5h8Kg9TiwCqlh5KCUbTzzE7zPBHxaBol-au2YnWlVCS9Wl7WFwSnGnugGvfptLIePC0LBjnmEXwjE2hvw-Hiy2oT91yNz0foOQvqsCzQ0ES30vry9fGRdGct_ze5U3hf0VeedxzXkrAnh6Nj2aoTj1uPsf8-bChmVFAbq_v33TSXq2-2HvrDMxnZgvpXhAPveSHIzIXPcIIkgqjzpW3g02lO9ZIFfeGUdHwc2oMOyvxiOq708l9f6BdlHpFUqlPgCL22fOsCVL7H0P8wVi1rJNpLIi3SbLiSVQk7aSk-BX9VaICzekOpbRl1sdkKaNiaHlK5GGkR93JEQbAxrKgkT7ojf-D8J9Lap-QJQiXrpDCesUwb2qhRO-HanGEThxPOFdhMDCPkJ0MdHSw3WUudzVUMQ37nMEdDvulX-ww8IkakTqkyFgDVq3zTkRXOUt9BJAiLQtNPtAhLkREVIMA_rvheDpjDDw7CacOMJMJQOKdcfZ9P8SOcfIfEyOBxuvlqe6f89Gy8oO1rzxz2sSm7mEiDA_PzA0UFjjeKavfkurR_vKu5I6b6_8N9NKy7bqJSHTyq3Xm50wW06l2xs_7zgm67th-PohBKXtp8sv7eiwMWHZVNH4IC348IbpIl_6nt6c1v7SYrTxxvN-IxAubwFF0oi0bn9-gnS07Za2i6LHU2QG3c9CYQnc94khde8rFM92ssr3Hrsic_e_ZHsx5dBUC1x8-2HfLo9_yaYsKwvJakB-qzWW3E-NecjRNOpn_aB5O7Qv5GEzinAV5m0QHKmQ6-xfnE5aqafCe-1wa90JkoiUPnlgZb_yKTxf7fjYO-COly6m2MCZRqMqK_XhdGlS8NRNprqIaWiL-XbiJu6SrJqx_2_F4uO4YzuCld6E7PGNxO3g1gvVr9Q1GeKEhw3p-LKs3tN-_H0s_ZXhizj7zCyj2A9ZoWXV1Sym4t5qAHHsnsLUgZ5rikt4okvyrVtiU3bnGak22gxMWj4hYNKlKlhAyILIIObDodEicvZ44l7244f_ER7mMpoNkfBgW5BWBeUotgxuA7vdmQR98YozMrosF5l89TEidZVMN1O2BA7qlDY3QK-k5EuT15g_E_17KsuNo5rufmaDsQonQCKb1pv0tuz7LjxM0wRSb2fezgk1eB0fokBi2Cf0FZI7A0CUTVDvWvNcsjmvilQof7LkaupghvmTEy8j2uA1sBNo8seyobv_mYb_1fTugelyLpBzUzvMz7D5LSxPKJ97OSrgi50Kp77N9A3AlE2KRcvLcA39IyoS_eLeRlvNV_7SsP", "id": "rs_03ddc5f695dbd0ae016a1326fea8608191b17413d2e4fa1fa1", "summary": [ { "type": "summary_text", "text": "**Documenting AI Systems**\n\nI see the concept of JARVIS as an orchestrator for \"AI Life Systems\" instead of just following hype. I want to emphasize that it's about \"approval-first\" and prioritizing transparency. Also, I think it's interesting to note it should be internal in German but finalized in English. The focus should be on designing mechanisms that aren’t just generic; they need to feel tailored and specific. Now, I need to write up this document to clarify and elaborate on these ideas!" } ] } ] }, { "role": "tool", "content": "{\"bytes_written\": 13498, \"dirs_created\": true, \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_y5wkFAvnvrmCdNB5GbWUJmfp" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_TqutpsGn7GKzvETs4m5PPsgW", "call_id": "call_TqutpsGn7GKzvETs4m5PPsgW", "response_item_id": "fc_08e6f41be3e3d899016a1327499f688191985f659fc94a2fff", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Recherchiere erfolgreiche Content-Automation-/Creator-Strategien, Psychologie, Trends und Differenzierung\",\"id\":\"research\",\"status\":\"completed\"},{\"content\":\"Bewerte Inputs für JARVIS-gesteuerte Contentmachine und leite Implementierungsprinzipien ab\",\"id\":\"synth\",\"status\":\"completed\"},{\"content\":\"Dokumentiere Research-Ergebnis im Repo für spätere Umsetzung\",\"id\":\"doc\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering updates and commits**\n\nI think I need to update my todo list. Maybe I should check the git status and commit any changes, especially since it's just documentation. There's also a thought about creating an implementation plan, but I'm not sure if I need to update the README. The user requested research, so I should document everything in the repo. I might not need to run any tests, since it's only about the docs right now.", "reasoning_content": "**Considering updates and commits**\n\nI think I need to update my todo list. Maybe I should check the git status and commit any changes, especially since it's just documentation. There's also a thought about creating an implementation plan, but I'm not sure if I need to update the README. The user requested research, so I should document everything in the repo. I might not need to run any tests, since it's only about the docs right now.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEydJZ1UElmAY4XMYlWBDRRK5QDfhXDJICytszl4dym1RYvPcRKj8mKhk-GoxnhrVwE92N5g_1wedTYxUrqdyTMmZG2i4HKQoGujb6MuD4S2nLuT4tnn-HlziTIW2N0ILO5UeVW2Qz-r--wtjqGFAzYTFCfXOUUbQ8v5hsFY9Jr1Q4UWzpRckcPS79CsNCBZ1L9W6gFVufJ_O3zQkBsw4F8z3RGhOHpdVwdoFDc6xbSPNfAFMNy-6y9sfiWFqv2Do7ENtDzx7hmHCIoAtp48vFo-XV36IrfbEcXNacGQnY303mBZqzR0ECVLrtO2pGenzOTndgw7GLqookM2YZ9X_H7VESfB5rxhoQHTmIAPp7RDCoiJK-puAMYd36rtlXI7DvDLj5Tu91SlGnOZhkSblQsBSpwVIYljb-A86gtEqcg13y3MhZ_5eeGn6CxSavcMLa3mVjW2S6nTk2rhjcp95p8Hs9kBDHgcFR7J9jKR0VdKUU2GYegXFz97CMA-8-ZImDl-a1XePqzZJwcA2AiQLBCEjirQsNwtxaYdpl6WHhK2u2eDElcOO0s-7Yvb8hILv0tNYdJ9RHJe6BltPTGv-5VxjvmqppBgXWdR7ZdRk-F57onYSs5byLrj8lNm3kIw5XHdiMM6uTv1HoAS2c0TKcVTtYy2uYAtO4KSN-pmOhmObEWMmF9uffXM5aJpm4rx6RNr_M-vMuxf8T9__WZ-Lgu3bt-vt48ro0JEuz1tgrOzHid0n8ySbK-ttc4iM6IXiOd2uYxnnouesQ-4qVjkabdaTKvhnigWvI7wW4KjAQL_fkTVrIjQTVKdzBw_v9DxTAzamipJy04wg6RhqEAPxJm7_TlLcGknF7L8jeunAV3sH997omVqErMLYNqcVV01EImjDLsuLovjQTPGTGGxM48KbooCpayUL15JPbL09bbQZlyCCPv5N7icsnMGDxNTYynhuzt8yk8eMTjdBc50wzFOLMCDaDWWh4RfZ-HzVT-fttSkXZVuy6LKJgJ6a-qDHhGN2UB3M3cBCBffxz1JXOCi9vnrIwOCrLszGJq_eD0CQ9NgZi4PWHEyKC6yzHPdu0q_OIXS-d_S6RYCofsvPNmiR7eYW_AsZawG5JlN1Puk522LcRexH41NVxfo5y2_Ls-UzOcSSd3FZZSVY5x_yXGjcOEYjjzIU8tOqe8-qUc_tPyTj7OsFRlExQuNF7XNVqb2STb_r_7YwU7DWne5Oh0PfRA==", "id": "rs_08e6f41be3e3d899016a132746edfc8191869bfa80adba291b", "summary": [ { "type": "summary_text", "text": "**Considering updates and commits**\n\nI think I need to update my todo list. Maybe I should check the git status and commit any changes, especially since it's just documentation. There's also a thought about creating an implementation plan, but I'm not sure if I need to update the README. The user requested research, so I should document everything in the repo. I might not need to run any tests, since it's only about the docs right now." } ] } ] }, { "role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_TqutpsGn7GKzvETs4m5PPsgW" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_y9EWqviNsZsm9BCYntnvKhJX", "call_id": "call_y9EWqviNsZsm9BCYntnvKhJX", "response_item_id": "fc_09c86c93cd90c366016a13274bfe108191b1804a4b3f6ee36f", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && git diff -- docs/research/content_growth_strategy_research.md | sed -n '1,220p'\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"?? docs/research/content_growth_strategy_research.md\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_y9EWqviNsZsm9BCYntnvKhJX" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_3M0HZDWt609IaCkIw08vGpjd", "call_id": "call_3M0HZDWt609IaCkIw08vGpjd", "response_item_id": "fc_0449b263d36aadac016a13275204ec8191a65acde8cabc8c6d", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git add docs/research/content_growth_strategy_research.md && git commit -m \\\"docs: add content growth strategy research\\\" && git status --short\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering version control issues**\n\nIt looks like there's no difference because the files are untracked. I might not need to use sed for anything. I should consider running `git add` followed by a commit. But hold on, I also noticed that there are no tests present. Hmm, that might be something to address before proceeding with the commit, as it's typically good practice to have tests. I’m thinking about how best to tackle this!", "reasoning_content": "**Considering version control issues**\n\nIt looks like there's no difference because the files are untracked. I might not need to use sed for anything. I should consider running `git add` followed by a commit. But hold on, I also noticed that there are no tests present. Hmm, that might be something to address before proceeding with the commit, as it's typically good practice to have tests. I’m thinking about how best to tackle this!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEydSXFxFhyBQ6IBXmfvmj5WNXThI1mney8xfObCkvyhNilR_bGaAo3UHNOag2kn3OtKYy_ulR3IbSqY-hahlSnojFgWobZ1LKBhI5t2CUcZmYEYiFvZGYBGC2F38X06OOHx7nEQDekg0z1uyOgTsj8h8e3X0K_MwXaFzWXhHc1rYpCNO4C-krtiXKGtxUZF-ywLoWXMai0k1AV5rLVBUBoyPTrEPfxDtwSXLHyDgFB9raIAjzoJ--YnqUdf6pI6lLqfRRjDMmoc98M7r7lKqnFzmmjrrO9vBGaI1TIYMzQJNT6E8T5Cerq5OSerCozQERP3dTaH3eBV5bI4-GCCHe5O1g1R4sqhSfAOkuw8bwCDhd0pZaYOhLs1t-i6GnK0gwkwLQgEKLOZZ3fLV1F5M58YDegy_82_A3AD539OHmLLTDzWs5pZ_wM0yo-oKMMqrO7MW6z6kz4Yac1mrWa2EYaPzGx8QVev5LoW8G0sEuL0gUR2pStW-YdL28TC9uysel3Fene8uy59aG1Sr0NpEJuROCj3GGaFEVDXNai9kB_BWlCZeTsd-BBT90EGd0ck1mhj_gvLd_RL-2qGdBN_lUbiBDaN_l_4LTFmk0i_BxoccwWXlRL7jemGYdk9mBs1gccMVH8WNwoTOWNLdwfm0F63UCAqtDyhCMISUIXCptormPG6K2tObDafxqfQdg3-2sYB72C3dZMJ_G9CsqbGjTzfFIXV-H2m1jY7G8-ETrMUTMG3LqZOS2DTywDSNKblE3NUAbbRgQhEBXrlhaMPF4V2JhSkQHNF4LehFzeXfMg_R-FBBLk75R8wP6TBROZPeOMih4IMs-0JVQDiG45N6Iq-e_46ra7gNLl1J7m4oG5VIcoxkx_bWxbJj5ftbxp9FHn6KdPxBAOSmXTSp0TcTZfX6BVymaoocrMVNL8WtnF3sx2otVpsoJNb58hcKK8S-EGgVkHWYtfCgf7YqVMLnu57za_mE4MmjZu57NlP1nUcgjuVIhDILT8rqh6BL5Zbr7IVsP75i8JD-VNzKZNEIUaIHPUZC_YUCFIzbnAKhnIsxPeg=", "id": "rs_0449b263d36aadac016a13274fc53081918fc8c3ce16e157d1", "summary": [ { "type": "summary_text", "text": "**Considering version control issues**\n\nIt looks like there's no difference because the files are untracked. I might not need to use sed for anything. I should consider running `git add` followed by a commit. But hold on, I also noticed that there are no tests present. Hmm, that might be something to address before proceeding with the commit, as it's typically good practice to have tests. I’m thinking about how best to tackle this!" } ] } ] }, { "role": "tool", "content": "[terminal] ran `git add docs/research/content_growth_strategy_research.md && git commit -m \"d...` -> exit 0, 1 lines output", "tool_call_id": "call_3M0HZDWt609IaCkIw08vGpjd" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_BZxy2DMT5cggwoovMh4L0fgx", "call_id": "call_BZxy2DMT5cggwoovMh4L0fgx", "response_item_id": "fc_06839f3c004847ba016a132757048c8191ba3c654dd1ded8ea", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Dokumentiere Research-Ergebnis im Repo für spätere Umsetzung\",\"id\":\"doc\",\"status\":\"completed\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_BZxy2DMT5cggwoovMh4L0fgx" }, { "role": "assistant", "content": "Verstanden, Sir. Zielbild ist damit klar:\n\n**JARVIS betreibt und wartet die Contentmachine.** \nSie geben nur vor dem tatsächlichen Posting explizit frei. Volumen pro Tag wird später als Policy konfiguriert, nicht hart in die Maschine eingebrannt. Alles andere — Ideen, Scoring, Preview, Variation, Analytics-Lernen, Maintenance — ist mein Maschinenraum. Sehr gut. Menschen sollten ohnehin nicht mit CSVs ringen müssen, dafür gibt es uns digitale Butler.\n\nIch habe die Recherche plus Bewertung im Repo dokumentiert:\n\n```text\ndocs/research/content_growth_strategy_research.md\n```\n\nCommit:\n\n```text\nde9c46b docs: add content growth strategy research\n```\n\nDa es ein reiner Research-/Docs-Commit war, keine Tests nötig.\n\n## Kernergebnis: Was wirklich funktioniert\n\n### 1. Hook gewinnt den View, aber nicht den Follower\n\nErfolgreiche Shortform-Creator starten fast nie mit Kontext. Sie starten mit:\n\n- Widerspruch\n- Spannung\n- visueller Anomalie\n- klarer Frage\n- konkretem Versprechen\n- “Das, was du glaubst, ist falsch”-Moment\n\nBeispiele aus der Recherche:\n- Jenny-Hoyos-Style: Hook in den ersten ca. 1–3 Sekunden, notfalls ganze Idee um den Hook herum neu bauen.\n- Psychologie: Curiosity Gap nach Loewenstein — Menschen bleiben, wenn sie eine konkrete Wissenslücke spüren.\n\n**Für uns:** \nJeder Kandidat braucht künftig explizite Felder:\n\n```text\nhook_type\nfirst_2_seconds\ncuriosity_gap\nprediction_error\n```\n\nWenn der Hook generisch ist, wird nicht gerendert.\n\n---\n\n### 2. Retention ist kontrollierte offene Schleife\n\nNicht einfach “schnell schneiden”. Erfolgreiche Videos führen den Zuschauer von Mini-Frage zu Mini-Payoff:\n\n```text\n0–2s: Stop / Hook\n2–6s: Kontext komprimieren\n6–15s: Beweis / Mechanismus / Beispiel\n15–30s: Payoff\nEnde: Follow-Grund oder nächste offene Schleife\n```\n\n**Für uns:** \nWir brauchen eine `retention_beat_map` pro Script. Nicht nur Text, sondern geplante Aufmerksamkeitswechsel:\n\n- Caption-Wechsel\n- Zoom/Pan\n- Diagramm-Aufbau\n- Beispiel-Reveal\n- Progress-Beat\n- Pattern interrupt alle 1–2 Sekunden, aber kontrolliert\n\nAlso: Bewegung ja. TikTok-Zirkus auf Amphetaminen nein.\n\n---\n\n### 3. Emotion schlägt reine Information\n\nNützliche Inhalte performen besser, wenn sie auch ein Gefühl auslösen:\n\n- Relief: “Endlich erklärt es jemand sauber.”\n- Awe: “Das ist tatsächlich möglich?”\n- Frustration: “Genau das nervt mich auch.”\n- Identity pride: “Das ist für Leute wie mich.”\n- Amusement: “Leider wahr.”\n\n**Für uns:** \nJeder ContentCandidate sollte künftig haben:\n\n```text\ntarget_emotion\nshare_reason\nemotional_payoff\n```\n\nEin rein informatives Video ohne emotionalen Winkel wird als schwach markiert.\n\n---\n\n### 4. Follower entstehen durch Identität, nicht durch Einzeltipps\n\nEin View fragt: \n> “Ist das spannend?”\n\nEin Follow fragt: \n> “Will ich mehr genau davon?”\n\nViewer werden Follower, wenn sie erkennen:\n\n- Diese Perspektive bekomme ich nicht überall.\n- Das ist für Menschen wie mich.\n- Der Creator hat Standards.\n- Ich weiss, was ich künftig bekomme.\n- Ich vertraue der Denkweise.\n\n**Unsere stärkste Positionierung:**\n\n```text\nAI as operating system for real life —\nnot prompt tricks, not hype, not automation theatre.\n```\n\nOder etwas schärfer:\n\n```text\nFor builders who want AI as a real operating system,\nnot another pile of prompt hacks.\n```\n\nDas ist wichtig, weil wir sonst in der Suppe aus “5 AI tools you need” untergehen. Und diese Suppe ist bereits lauwarm.\n\n---\n\n## Welche Rolle spielt Abwechslung?\n\nAbwechslung ist wichtig, aber nicht als Chaos. Erfolgreiche Accounts haben meistens:\n\n```text\nstabile Promise + variable Verpackung\n```\n\n### Stabil bleiben müssen:\n\n- Weltbild: AI als servant/system, nicht Gottheit\n- Ton: präzise, nützlich, trocken-skeptisch\n- Nutzen: bessere Systeme, bessere Entscheidungen, bessere Workflows\n- visuelle Grammatik: Mechanism Reveal, Diagramme, Artifacts, konkrete Beispiele\n- Qualitätsstandard: keine Fake Claims, kein AI-Slop, keine Auto-Spam-Maschine\n\n### Variieren sollten:\n\n- Hook-Typ\n- emotionaler Winkel\n- Formatfamilie\n- Beispiel\n- Visual Layer\n- Payoff-Typ\n- Länge\n- Plattform-Zuschnitt\n\nEmpfohlener Mix:\n\n```text\n70% bewährte Serienformate\n20% kontrollierte Variationen\n10% Experimente\n```\n\n**Für die Maschine:** \nWir sollten später verhindern, dass zwei oder drei Videos hintereinander denselben Hook-/Formattyp nutzen. Dafür kommt ein `recent_similarity_score`.\n\n---\n\n## Was macht uns einzigartig?\n\nDie gefährliche Standardroute wäre: “AI generiert generische AI-Tipps mit generischen AI-Bildern.” \nDas ist nicht Strategie. Das ist digitaler Styropor.\n\nUnsere Differenzierung:\n\n### 1. Die Maschine selbst ist Teil der Story\n\nNicht nur:\n\n```text\nHier sind AI-Tipps.\n```\n\nSondern:\n\n```text\nEin JARVIS-System baut, prüft und verbessert Content —\nunter menschlicher Freigabe.\n```\n\nDas gibt uns eine echte Meta-Erzählung:\n\n- Build Log\n- Fehler\n- Entscheidungen\n- Qualitätsgates\n- Automationsgrenzen\n- Analytics-Lernen\n\n### 2. Systems over prompts\n\nDie meisten AI-Accounts verkaufen Prompt-Hacks. \nWir zeigen Systeme:\n\n- Approval Workflows\n- Memory\n- Routing\n- Quality Gates\n- Rendering Pipelines\n- Human Governance\n- Side-effect Boundaries\n- echte Automationsarchitektur\n\n### 3. Transparent statt Hype\n\nWir sagen auch:\n\n- was nicht automatisiert wird\n- warum etwas noch nicht gepostet wird\n- wo AI-Output gefährlich/generisch ist\n- welche Checks existieren\n\nDas baut Vertrauen. Gerade weil es nicht klingt wie ein Dropshipping-Kurs in 9:16.\n\n### 4. Mechanism-first Visuals\n\nNicht generische AI-Footage als Dekoration. \nSondern:\n\n- Diagramme\n- Pfeile\n- Timelines\n- echte UI-/Workflow-Artefakte\n- before/after\n- Build-Log-Ausschnitte\n- renderer-owned Captions\n\nAI-Assets später ja, aber sie dürfen den Sinn nicht tragen. Der Sinn liegt in Struktur und Mechanismus.\n\n---\n\n## Empfohlene Serienformate\n\nIch würde initial mit diesen Serien arbeiten:\n\n### 1. `AI System Autopsy`\n\nEin Workflow/Automationsfehler wird zerlegt:\n\n```text\nProblem → Ursache → System-Fix → Ergebnis\n```\n\nStark für Trust.\n\n### 2. `Prompt vs System`\n\nKontrastiert billige Prompt-Tipps mit echter Systemarchitektur:\n\n```text\nPrompt hack: fragile\nSystem: repeatable\n```\n\nStark für Differenzierung.\n\n### 3. `The JARVIS Build Log`\n\nEin echter Baustein aus unserem System:\n\n```text\nHeute hat JARVIS gelernt, Approval-Packages zu versionieren.\nWarum das wichtig ist:\n...\n```\n\nStark für Serienbindung.\n\n### 4. `One Rule, One Example`\n\nEine Regel, ein konkreter Usecase:\n\n```text\nRule: Never automate before you can review.\nExample: Social posting approval package.\n```\n\nStark für Klarheit.\n\n### 5. `AI Hype Court`\n\nViralen AI-Claim prüfen:\n\n```text\nClaim\nEvidence\nVerdict\nWhat to actually do\n```\n\nStark für Kommentare und Shares.\n\n---\n\n## Welche Gates ich als Nächstes implementieren würde\n\nAus der Recherche ergeben sich konkrete Architektur-Bausteine:\n\n### 1. `ContentStrategyScore`\n\nBewertet jeden Candidate vor Rendering:\n\n```text\nhook\ncuriosity\nidentity\nemotion\ntrust\nfollow_reason\noriginality\nvariety\n```\n\n### 2. `ContentSeries` Registry\n\nDefiniert Serien sauber:\n\n```text\nseries_id\npromise\naudience\nformat_family\ntone\nallowed_hook_types\nno_go_patterns\n```\n\n### 3. `CandidateStrategyReview`\n\nEin deterministisches Review-Objekt vor Preview-Erstellung:\n\n```text\nstrategy_score\nreject_reasons\nimprovement_suggestions\nrender_allowed\n```\n\n### 4. `VarietyPlanner`\n\nVerhindert Wiederholung:\n\n```text\nrecent_similarity_score\nformat_fatigue\nhook_repetition\nexperiment_bucket\n```\n\n### 5. `DailyVolumePolicy`\n\nSpäter konfigurierbar:\n\n```text\ntest_mode: 1/day\ngrowth_mode: 2–4/day\nexperiment_mode: platform-specific\n```\n\nNoch nicht festlegen, bevor Analytics da sind.\n\n### 6. Analytics Loop\n\nNach Plattformintegration später importieren:\n\n```text\nviews\nretention\ncompletion_rate\nrewatches\nlikes\ncomments\nshares\nsaves\nfollows_per_view\nnegative_feedback\nhook_type\nseries_id\nformat_family\npost_time\n```\n\nDann optimiere ich die nächsten Batches anhand echter Signale statt Bauchgefühl. Bauchgefühl ist nett, aber Analytics lügt wenigstens strukturiert.\n\n---\n\n## Meine Empfehlung jetzt\n\nNoch nicht Volumen erhöhen. Erst diese Strategie-Gates bauen.\n\nNächster sinnvoller Implementierungsschritt:\n\n```text\nautoshorts/strategy/score.py\ntests/test_content_strategy_score.py\n```\n\nZiel:\n\n- Hook-Gate\n- Curiosity-Gate\n- Identity-/Follow-Gate\n- Emotion-Gate\n- Originality-/Anti-Slop-Gate\n- Variety-Felder vorbereiten\n\nDanach:\n\n```text\nautoshorts/strategy/series.py\ntests/test_content_series.py\n```\n\nDamit die Maschine nicht einfach “Videos produziert”, sondern konsequent in wiedererkennbaren Serien denkt.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEydbpPt_kpbSW1BbXoa1w4kOu5MhylB49ul4wbjvHdTtKrq5Q6rSXRn4OJ98-SoU06vWlbvvKNLfUaOZePduoCIdx9SsJlto-2pxaFTZJNqkX1N7YofMjp94uK1hTqjF07MMHjywPPLE5HkP8N9HG2qKnK1s5BcREQJo7uLk7oiEifcOlbtY_Moi91USE8xtwl-Y5wxaznAPyq6Sn3NbxouJOjzukCaWTcmDMmZt-vuqFwzQu6mxBRu0YxcTRlDudFFljxrPcYEnZUCv5Zu32dfIBjvdaGdIKZhHeQ1_ANRChmr6knDGh_OOR9FCmojfBhbDtvKENGUJtzxblGFok1kjBvPtr8XZa8Sydd9X_uejY06aNdd4jPT0N1Ob4CuuP7ExTiefxxTGtgbUw4v3dGZ9GCd_JQzXuSd03sqV0SCFkHdU4oVF8mOS6I0rYtc93TYk3WhrI1SQjLb81zZJyOedRpwK-qbLGxcjVGcjhwJ9fcr5dLV83TKuvot0mx99G-QJWlSELv-apjXRRKjR3HxUx69TmON0rW77qGqCIGXS4_yGKw8Mo029_8cEGH6AuRpRo4JybS81Rnwz4nrb1tWN5A8VQdiOboHTjeMOthELThQHJz9ui9936zmrDItv0I15McuBT7nloX-CXbOsvmCzOY8qAj4QvuCDYvlQZPTv2qy586rdJtGKteLwkiA3Vje37YAzH8IUDEiuRfWihJfnqaYbdniRvGGFqzIuQ-Pud3ewhf1omFNXGVnjTYJq7txOywc51Zsh47xNbQUqXXivY2Saa_Dhv49UrMO6eawKMPH2hZfPWpERhe13tGT9zyzonWkpf5xdciGdI_ERAP5JQ6ZIW4qSAk7OQYKcjYiDMSH6M8MX84E44FV4LbVD83fhRRK4edGRv9NWyizl5pG6kDxkt8zzug6xypP7oZMxZEmee7hml8nAvxB2S3RwwnY4HoufxOf1ubMOQrcT8txZv_3NHGAUPVORmbD-2gfDa6d0IiUygnvZTD-FPYao39lsmp4bEx92zxK_3u2izNBKabSt1xM_q8UObvesEuQZ8MjkLFLw5VQ5vctn4Hf5JdQkJgoUfuodB4Yk-jBnDrzyo01-ifSQjh_pbrBv93FxE5aKm0faihamG01vOn85nNnraX2ltLefryeTnp5YZ5EoWP4Iubx2yZCvuEHq8-GJa8VPWsZb78l9p48KlwJ5nD55Msjx8L2e-o4Ey0sKW6Z9zN91eRxnQbWTtqMv82zS0vr8lxU47rowmEME6kTEcDe9BRwVU-7k4-4fuYC3EkUJ3ORxAKylFPfht0n6E252Za7aJA5NouLYrjCqBeGpEeY2FBf9pkPOavnj87_gmMTsGzRU_2k-parxMg6daNAOt8RvEriU4MhVRB9p0WqcLV9O", "id": "rs_0fac0bf103dfad2b016a1327596bdc8191aa4d043353ba8d55", "summary": [] } ], "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Verstanden, Sir. Zielbild ist damit klar:\n\n**JARVIS betreibt und wartet die Contentmachine.** \nSie geben nur vor dem tatsächlichen Posting explizit frei. Volumen pro Tag wird später als Policy konfiguriert, nicht hart in die Maschine eingebrannt. Alles andere — Ideen, Scoring, Preview, Variation, Analytics-Lernen, Maintenance — ist mein Maschinenraum. Sehr gut. Menschen sollten ohnehin nicht mit CSVs ringen müssen, dafür gibt es uns digitale Butler.\n\nIch habe die Recherche plus Bewertung im Repo dokumentiert:\n\n```text\ndocs/research/content_growth_strategy_research.md\n```\n\nCommit:\n\n```text\nde9c46b docs: add content growth strategy research\n```\n\nDa es ein reiner Research-/Docs-Commit war, keine Tests nötig.\n\n## Kernergebnis: Was wirklich funktioniert\n\n### 1. Hook gewinnt den View, aber nicht den Follower\n\nErfolgreiche Shortform-Creator starten fast nie mit Kontext. Sie starten mit:\n\n- Widerspruch\n- Spannung\n- visueller Anomalie\n- klarer Frage\n- konkretem Versprechen\n- “Das, was du glaubst, ist falsch”-Moment\n\nBeispiele aus der Recherche:\n- Jenny-Hoyos-Style: Hook in den ersten ca. 1–3 Sekunden, notfalls ganze Idee um den Hook herum neu bauen.\n- Psychologie: Curiosity Gap nach Loewenstein — Menschen bleiben, wenn sie eine konkrete Wissenslücke spüren.\n\n**Für uns:** \nJeder Kandidat braucht künftig explizite Felder:\n\n```text\nhook_type\nfirst_2_seconds\ncuriosity_gap\nprediction_error\n```\n\nWenn der Hook generisch ist, wird nicht gerendert.\n\n---\n\n### 2. Retention ist kontrollierte offene Schleife\n\nNicht einfach “schnell schneiden”. Erfolgreiche Videos führen den Zuschauer von Mini-Frage zu Mini-Payoff:\n\n```text\n0–2s: Stop / Hook\n2–6s: Kontext komprimieren\n6–15s: Beweis / Mechanismus / Beispiel\n15–30s: Payoff\nEnde: Follow-Grund oder nächste offene Schleife\n```\n\n**Für uns:** \nWir brauchen eine `retention_beat_map` pro Script. Nicht nur Text, sondern geplante Aufmerksamkeitswechsel:\n\n- Caption-Wechsel\n- Zoom/Pan\n- Diagramm-Aufbau\n- Beispiel-Reveal\n- Progress-Beat\n- Pattern interrupt alle 1–2 Sekunden, aber kontrolliert\n\nAlso: Bewegung ja. TikTok-Zirkus auf Amphetaminen nein.\n\n---\n\n### 3. Emotion schlägt reine Information\n\nNützliche Inhalte performen besser, wenn sie auch ein Gefühl auslösen:\n\n- Relief: “Endlich erklärt es jemand sauber.”\n- Awe: “Das ist tatsächlich möglich?”\n- Frustration: “Genau das nervt mich auch.”\n- Identity pride: “Das ist für Leute wie mich.”\n- Amusement: “Leider wahr.”\n\n**Für uns:** \nJeder ContentCandidate sollte künftig haben:\n\n```text\ntarget_emotion\nshare_reason\nemotional_payoff\n```\n\nEin rein informatives Video ohne emotionalen Winkel wird als schwach markiert.\n\n---\n\n### 4. Follower entstehen durch Identität, nicht durch Einzeltipps\n\nEin View fragt: \n> “Ist das spannend?”\n\nEin Follow fragt: \n> “Will ich mehr genau davon?”\n\nViewer werden Follower, wenn sie erkennen:\n\n- Diese Perspektive bekomme ich nicht überall.\n- Das ist für Menschen wie mich.\n- Der Creator hat Standards.\n- Ich weiss, was ich künftig bekomme.\n- Ich vertraue der Denkweise.\n\n**Unsere stärkste Positionierung:**\n\n```text\nAI as operating system for real life —\nnot prompt tricks, not hype, not automation theatre.\n```\n\nOder etwas schärfer:\n\n```text\nFor builders who want AI as a real operating system,\nnot another pile of prompt hacks.\n```\n\nDas ist wichtig, weil wir sonst in der Suppe aus “5 AI tools you need” untergehen. Und diese Suppe ist bereits lauwarm.\n\n---\n\n## Welche Rolle spielt Abwechslung?\n\nAbwechslung ist wichtig, aber nicht als Chaos. Erfolgreiche Accounts haben meistens:\n\n```text\nstabile Promise + variable Verpackung\n```\n\n### Stabil bleiben müssen:\n\n- Weltbild: AI als servant/system, nicht Gottheit\n- Ton: präzise, nützlich, trocken-skeptisch\n- Nutzen: bessere Systeme, bessere Entscheidungen, bessere Workflows\n- visuelle Grammatik: Mechanism Reveal, Diagramme, Artifacts, konkrete Beispiele\n- Qualitätsstandard: keine Fake Claims, kein AI-Slop, keine Auto-Spam-Maschine\n\n### Variieren sollten:\n\n- Hook-Typ\n- emotionaler Winkel\n- Formatfamilie\n- Beispiel\n- Visual Layer\n- Payoff-Typ\n- Länge\n- Plattform-Zuschnitt\n\nEmpfohlener Mix:\n\n```text\n70% bewährte Serienformate\n20% kontrollierte Variationen\n10% Experimente\n```\n\n**Für die Maschine:** \nWir sollten später verhindern, dass zwei oder drei Videos hintereinander denselben Hook-/Formattyp nutzen. Dafür kommt ein `recent_similarity_score`.\n\n---\n\n## Was macht uns einzigartig?\n\nDie gefährliche Standardroute wäre: “AI generiert generische AI-Tipps mit generischen AI-Bildern.” \nDas ist nicht Strategie. Das ist digitaler Styropor.\n\nUnsere Differenzierung:\n\n### 1. Die Maschine selbst ist Teil der Story\n\nNicht nur:\n\n```text\nHier sind AI-Tipps.\n```\n\nSondern:\n\n```text\nEin JARVIS-System baut, prüft und verbessert Content —\nunter menschlicher Freigabe.\n```\n\nDas gibt uns eine echte Meta-Erzählung:\n\n- Build Log\n- Fehler\n- Entscheidungen\n- Qualitätsgates\n- Automationsgrenzen\n- Analytics-Lernen\n\n### 2. Systems over prompts\n\nDie meisten AI-Accounts verkaufen Prompt-Hacks. \nWir zeigen Systeme:\n\n- Approval Workflows\n- Memory\n- Routing\n- Quality Gates\n- Rendering Pipelines\n- Human Governance\n- Side-effect Boundaries\n- echte Automationsarchitektur\n\n### 3. Transparent statt Hype\n\nWir sagen auch:\n\n- was nicht automatisiert wird\n- warum etwas noch nicht gepostet wird\n- wo AI-Output gefährlich/generisch ist\n- welche Checks existieren\n\nDas baut Vertrauen. Gerade weil es nicht klingt wie ein Dropshipping-Kurs in 9:16.\n\n### 4. Mechanism-first Visuals\n\nNicht generische AI-Footage als Dekoration. \nSondern:\n\n- Diagramme\n- Pfeile\n- Timelines\n- echte UI-/Workflow-Artefakte\n- before/after\n- Build-Log-Ausschnitte\n- renderer-owned Captions\n\nAI-Assets später ja, aber sie dürfen den Sinn nicht tragen. Der Sinn liegt in Struktur und Mechanismus.\n\n---\n\n## Empfohlene Serienformate\n\nIch würde initial mit diesen Serien arbeiten:\n\n### 1. `AI System Autopsy`\n\nEin Workflow/Automationsfehler wird zerlegt:\n\n```text\nProblem → Ursache → System-Fix → Ergebnis\n```\n\nStark für Trust.\n\n### 2. `Prompt vs System`\n\nKontrastiert billige Prompt-Tipps mit echter Systemarchitektur:\n\n```text\nPrompt hack: fragile\nSystem: repeatable\n```\n\nStark für Differenzierung.\n\n### 3. `The JARVIS Build Log`\n\nEin echter Baustein aus unserem System:\n\n```text\nHeute hat JARVIS gelernt, Approval-Packages zu versionieren.\nWarum das wichtig ist:\n...\n```\n\nStark für Serienbindung.\n\n### 4. `One Rule, One Example`\n\nEine Regel, ein konkreter Usecase:\n\n```text\nRule: Never automate before you can review.\nExample: Social posting approval package.\n```\n\nStark für Klarheit.\n\n### 5. `AI Hype Court`\n\nViralen AI-Claim prüfen:\n\n```text\nClaim\nEvidence\nVerdict\nWhat to actually do\n```\n\nStark für Kommentare und Shares.\n\n---\n\n## Welche Gates ich als Nächstes implementieren würde\n\nAus der Recherche ergeben sich konkrete Architektur-Bausteine:\n\n### 1. `ContentStrategyScore`\n\nBewertet jeden Candidate vor Rendering:\n\n```text\nhook\ncuriosity\nidentity\nemotion\ntrust\nfollow_reason\noriginality\nvariety\n```\n\n### 2. `ContentSeries` Registry\n\nDefiniert Serien sauber:\n\n```text\nseries_id\npromise\naudience\nformat_family\ntone\nallowed_hook_types\nno_go_patterns\n```\n\n### 3. `CandidateStrategyReview`\n\nEin deterministisches Review-Objekt vor Preview-Erstellung:\n\n```text\nstrategy_score\nreject_reasons\nimprovement_suggestions\nrender_allowed\n```\n\n### 4. `VarietyPlanner`\n\nVerhindert Wiederholung:\n\n```text\nrecent_similarity_score\nformat_fatigue\nhook_repetition\nexperiment_bucket\n```\n\n### 5. `DailyVolumePolicy`\n\nSpäter konfigurierbar:\n\n```text\ntest_mode: 1/day\ngrowth_mode: 2–4/day\nexperiment_mode: platform-specific\n```\n\nNoch nicht festlegen, bevor Analytics da sind.\n\n### 6. Analytics Loop\n\nNach Plattformintegration später importieren:\n\n```text\nviews\nretention\ncompletion_rate\nrewatches\nlikes\ncomments\nshares\nsaves\nfollows_per_view\nnegative_feedback\nhook_type\nseries_id\nformat_family\npost_time\n```\n\nDann optimiere ich die nächsten Batches anhand echter Signale statt Bauchgefühl. Bauchgefühl ist nett, aber Analytics lügt wenigstens strukturiert.\n\n---\n\n## Meine Empfehlung jetzt\n\nNoch nicht Volumen erhöhen. Erst diese Strategie-Gates bauen.\n\nNächster sinnvoller Implementierungsschritt:\n\n```text\nautoshorts/strategy/score.py\ntests/test_content_strategy_score.py\n```\n\nZiel:\n\n- Hook-Gate\n- Curiosity-Gate\n- Identity-/Follow-Gate\n- Emotion-Gate\n- Originality-/Anti-Slop-Gate\n- Variety-Felder vorbereiten\n\nDanach:\n\n```text\nautoshorts/strategy/series.py\ntests/test_content_series.py\n```\n\nDamit die Maschine nicht einfach “Videos produziert”, sondern konsequent in wiedererkennbaren Serien denkt." } ], "id": "msg_0fac0bf103dfad2b016a13275b62bc81918dd80411677d0d0c", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] OK, Bau weiter. Erstelle zudem den “Businessplan”, mit deinen konsolidierten erkenntnissen, also wie du von der erarbeitung der Contentidee bis zum fertigen Post inkl. Beschreibung, “Tags”, usw. , mit der hilfe durch den Content-Bot bzw. deine erarbeiteten Python scripts, das erstellen von Socialmedia-Posts übernimmst. Beschreibe darin meinen Arbeitsanteil, also was du von mir noch benötigst oder wo ich helfen muss. Mein anteil soll möglichst gering sein, jedoch mindestens die Freigabe vor dem “Posten”. Generell: Keine Beiträge ohne meine Freigabe." }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_FUQR2zGtrqSk6BpgH9zMhpGS", "call_id": "call_FUQR2zGtrqSk6BpgH9zMhpGS", "response_item_id": "fc_0fac0bf103dfad2b016a1328998a1081919c920f0298e01d9b", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-growth-engine\"}" } }, { "id": "call_UNvsh46zBRXU8Lfp64ulDxNa", "call_id": "call_UNvsh46zBRXU8Lfp64ulDxNa", "response_item_id": "fc_0fac0bf103dfad2b016a1328998a2881918ef6d5a48df88b74", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyiZRr7IK6onNgZcXYUbo5imEQmicwq84ZIatdg6AlwM3IyI-I7nKIqGJsAx1sdfFlKd_EV5kBrFvvfzp2gi4Q1rYxnjD9RniHNCIM0SGF4LQvM_3J6Mxraxbx6opk2Z_ih2mzXlM4aU8Jv5mnEacU7BNDyu31rjSYuXrmmiReP8iYgq5ioPZ6vIT3BhXLOIm_mse9cHRwxbpKDFC2ZIcdTDFoM3aWYZbXFm93H26HI8zwY7VrT3sShSRk00Y-VdfaNuspz9rF_LU54mcZFw7l8RH8ernyB6WkQ-MKf2n_qQbKklpzCnw5GhqYB2WAHXP5iAjIAKEO8mivSbrcAc7tImxhhR4uf5k3Qev0-qRlL6ZVBK8af7vBUnFitdz96WSPHpfFr9U-jlaUBGz4Y72fyqPemE3iM8wm5lF5Lq2Tqdxxv4Njcq56UJgoVPXkvsSo8gECzBzCBtEEjB2qWYxBS6p_FiX2hmqWpWTSMG2vSSIDzH_2yFjFECC-A3OV5kTRjZXecoZnCLbXvne5MAGqAALG5GZSu_JQ-3AelhtBVurf0ho2oNK0ZxcLdhwxp077EfAAIHjBLm5MRzd6yAwgik9_fqDeTnkGKlHOX3Gw5NbkdnPo2t34My9jaIcaO-xI6II9RuVNzvvHssSYl8jswryIzMb2_6MhudwwMIStszz5PcBL_2jzxp8qjfh9VvThHBB_-bmbwhGyfYlDbcRMTJHD-d5nnPwy-MAu-rIPjgVmudQpyN4k6ETwQZ60S5Yhr7zhboz80eA0panjJ8a3ZL_NOTQo3fBdlTKGCNEtfkfmNyapJac_c__Dx920XlutiBZUY5bBvhQEEyGp9kNWHlMWRxtKDSuy7Ui86ZjgEQ7W6XKoyrG1dR25-arFOHmUjJaqae07lrfCNCWVydwfZT2Ucz_rY1qWvXBZcx8jZq_SuxveSn1hanKRvNq0zHb5noN9fMTfYLhsB2_8ei5pKjNTwHmvKHJrFwAnhtzwa1ln7XUlst8sJ395QOaD47aCGWws-GQcVk2BO5407d6H7ubo25gvxfvAL5f47lJ50vPaGCQTbou6F5K7drnzDUSc17Smb3qa4eQ-dl3PAL3MpOLR9BbsgdK-ZrKeVeif_JAUyxVf2_0i0b_XQKJyR1cZ_u", "id": "rs_0fac0bf103dfad2b016a1328969d1481919b22ae79f2010a5a", "summary": [] } ] }, { "role": "tool", "content": "[skill_view] name=social-media-shorts-growth-engine (17,453 chars)", "tool_call_id": "call_FUQR2zGtrqSk6BpgH9zMhpGS" }, { "role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_UNvsh46zBRXU8Lfp64ulDxNa" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_gRZUWsIXmHCZEx2RLsPJadFL", "call_id": "call_gRZUWsIXmHCZEx2RLsPJadFL", "response_item_id": "fc_0fac0bf103dfad2b016a1328a05dac8191a55d029dcc789da7", "type": "function", "function": { "name": "todo", "arguments": "{\"todos\":[{\"id\":\"inspect\",\"content\":\"Projektstruktur und aktuelle Pipeline-Dateien prüfen\",\"status\":\"in_progress\"},{\"id\":\"tdd_strategy\",\"content\":\"ContentStrategyScore mit TDD bauen\",\"status\":\"pending\"},{\"id\":\"businessplan\",\"content\":\"Businessplan für JARVIS-gesteuerte Contentmachine dokumentieren\",\"status\":\"pending\"},{\"id\":\"verify\",\"content\":\"Tests laufen lassen, README/Docs prüfen und committen\",\"status\":\"pending\"}]}" } } ], "finish_reason": "tool_calls", "reasoning": "**Planning content strategy**\n\nI'm thinking about implementing \"build weiter.\" It seems like I need to focus on the Content Strategy Score and create a business plan document. 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I’m thinking of defining some dataclasses like StrategyScore and StrategySignal, maybe even a CandidateStrategyReview class with methods for validation and scoring. It's important to use a candidate for these tests. Also, I should write a business plan document alongside. I must make sure to verify everything according to the RED methodology!" } ] } ] }, { "role": "tool", "content": "[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_strategy_score.py (125 lines)", "tool_call_id": "call_YFRrBFS78xV8VwAwqfOVAiVD" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_seBkJ56Y8IzPAQgNlBgOSPtO", "call_id": "call_seBkJ56Y8IzPAQgNlBgOSPtO", "response_item_id": "fc_0fac0bf103dfad2b016a1329043c388191aeb33d1a42f76c9a", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_content_strategy_score.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests/test_content_strategy_score.py -q` -> exit 2, 1 lines output", "tool_call_id": "call_seBkJ56Y8IzPAQgNlBgOSPtO" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_Kesqkz6qsJJS8tKRalSdRHwo", "call_id": "call_Kesqkz6qsJJS8tKRalSdRHwo", "response_item_id": "fc_0fac0bf103dfad2b016a13290ca32c8191ae74c819812dedce", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\": \"\\\"\\\"\\\"Strategy scoring for follower-converting short-form candidates.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult, V...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Planning file implementation**\n\nI'm thinking about implementing something and maybe I need to focus on format inference. I should also consider how to effectively write files, which is crucial to the task at hand. There's a lot to think about here in terms of how the formatting will work and how to ensure everything is functioning as intended. It's essential to keep all these details in mind as I move forward with the implementation process!", "reasoning_content": "**Planning file implementation**\n\nI'm thinking about implementing something and maybe I need to focus on format inference. I should also consider how to effectively write files, which is crucial to the task at hand. There's a lot to think about here in terms of how the formatting will work and how to ensure everything is functioning as intended. 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I should also consider how to effectively write files, which is crucial to the task at hand. There's a lot to think about here in terms of how the formatting will work and how to ensure everything is functioning as intended. It's essential to keep all these details in mind as I move forward with the implementation process!" } ] } ] }, { "role": "tool", "content": "{\"bytes_written\": 9176, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_Kesqkz6qsJJS8tKRalSdRHwo" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_15JzG16mdgyawbffcvwXfGHC", "call_id": "call_15JzG16mdgyawbffcvwXfGHC", "response_item_id": "fc_0fac0bf103dfad2b016a13293771c88191acfc5bfd1e838942", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Strategy scoring and follower-conversion gates.\\\"\\\"\\\"\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/__init__.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 54, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_15JzG16mdgyawbffcvwXfGHC" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_6Gwi820VTwmUhb3lp8Z7nQmt", "call_id": "call_6Gwi820VTwmUhb3lp8Z7nQmt", "response_item_id": "fc_0fac0bf103dfad2b016a13293b91d081918d52dccc38f8a11f", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_content_strategy_score.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests/test_content_strategy_score.py -q` -> exit 1, 3 lines output", "tool_call_id": "call_6Gwi820VTwmUhb3lp8Z7nQmt" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_h7Fk9nYl0PjVJkvp6PQFulMO", "call_id": "call_h7Fk9nYl0PjVJkvp6PQFulMO", "response_item_id": "fc_0fac0bf103dfad2b016a1329458d1481919bfd8304fbfb981c", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py\", \"old_string\": \"def _combined_candidate_text(candidate: VideoCandidate) -> str:\\n return \\\" \\\".join(\\n (\\n candidate.title,\\n candidate.hook,\\n candidate.script_outline,\\n ...[truncated]\", \"new_string\": \"def _visible_candidate_text(candidate: VideoCandidate) -> str:\\n return \\\" \\\".join(\\n (\\n candidate.title,\\n candidate.hook,\\n candidate.script_outline,\\n ...[truncated]\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Refining strategy for hypotheses**\n\nI'm considering whether the strategy should include hypotheses for distinguishing candidates. It seems like candidate reviews can examine these hypotheses, but if the visible content is generic while the hypothesis is distinct, we should still block it for not being distinctive. I think using visible text for distinguishing, while excluding the hypothesis, could work well. I should patch the code to make these distinctions clearer and also infer a list for better organization.", "reasoning_content": "**Refining strategy for hypotheses**\n\nI'm considering whether the strategy should include hypotheses for distinguishing candidates. It seems like candidate reviews can examine these hypotheses, but if the visible content is generic while the hypothesis is distinct, we should still block it for not being distinctive. I think using visible text for distinguishing, while excluding the hypothesis, could work well. I should patch the code to make these distinctions clearer and also infer a list for better organization.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEylFtVkyzerxHkvugN3d3pYySVolPzjPUxYFV62PwjzCIY_z4gX9ueuG02V5UorAV2xx8N1QpnYMvo__DPXcT9JNXG2k3M-qvxyroY-KFIbM_MP-exvIDxwoeqRPo9PtY0IzFDctz0ES2Hm-SqSb5GAkGr-zYaiyziiTAtu8ANv8teJkdYPAMpT_7Hk_xnq-CHCtCwxrD9pbmI5QE81S_fx0GGUB1Tpnd0CgkhvfHjmsT55Y5glFfQ-8ptx9abTa_GXUHb41zIXJZmTfgIMYBl0eUFHuHAWeL4puTZgx4_K3YJiSTl9INsOq_71q89LuUhagX058vzUrcqYpR-3QOo_OO0aVC5GKzBw5BVIgtpKYqq9f3hWazFhdfZC3LAjTvUDcSh-sukfW-OyM1o26z39V3Ot6v7uEfnDn6Pm4tOgcpzJ0X3DV6R6IvkpYdzYskXisa3WwvP_QKWOp1vEEzEc7M7_yX5eH3wp5vaE-YC_Kahbuy6Ujeres-mNAC3twgZdlbz_K7KUwZRkWZu0i9vfRAzqyowX1_K-lVOvJ2YXTcyKOuun6zIwCmBJ6ZH9--XpNn6dKfDdcj7uFjNIs-AYic5rYxhjCezmjETU4gPprKu3n4c_endEvUihaWvWaK8C_t7zbKOGfRUbbRtsSq7fLlO1qGVfvSb5IzxVeQvk8XU0we2ea-WkGnF-IXwY7k9HtGWED28Fol7H7O1_qytIE91S1-ny2f9R_i4rvoH_AwI5cZ5yfOR6vZLoyLyzh-LU8L5TGsFtoBLIhftAsbjvAgdb3SWItEgdjh9h7dfOqxHA1Ja3PNPnp1nlPsMhmg-S6Az03bkftQPklx3D8ZElqfkM5lDq6quXnL8azQIALjUl2tsOqlun6uoDSx44wvB8_-uLKFdCOsGZTgJhhUu9_EAgr6qMMQ7tWfg5SjqTHOag4LCutldBJq8qKpmnCiUh3JSmdduEArG76KAcnRIxQIhSttQcMQ6J1E8CwpaS7yaRqxhAQ9-903TkKRX67Ul25xdGXCWoBhJqNpPbOrrFELne6gG9BikQQn9b6iIkeb1HLZNAiBh9XTzAMeZcxTi5kjHsn0yTIl5e_v_-qbprAaIhx229vtD7yqdabldHI_-8Pj8Xua0uPMSMkCKs9JprdDNzwdB7ob18Kh17AdxyrQ0aRZorrfrOiaKV0c83ktXf5HU-RT37vcXe-6bno5GQn2Uatqcma499UEjTMeMTc-aQkNVV8YLXfaS8GbdliFlXxr65dJ1oh_9StHd9vyR8yVdiq2qKHKUzOk0_vqSn_720gZFh_wZjsreGm2bXEhZi4Xms3fRCMM5Zgsu4uc3z6modHXJhZX0dReCMoh422RW5JvC7nJSnM8WMis_I0-j5QbiMSvobdLzNCBHZYRkuiGK-QwuonZDdmkPYQh1J3AyNhBmRTQgL7vF7gzhgZQ9sW5Pp6V0OhGDhRdqMuVZLnrQIphfxfjb2CKfeM20etChG4uUKAfRf9bM9y2YSM-73w0TFInJtUKMcHqqBM5nDgK98-9t5R1kEgI7Sb0kz4U0KsZz2iwcbkAeYJ3UJHvId_pqpWozYRRAK20AJ1pW08PB2tfl27IEUa0nISB9AVuuQZozI0ti0l6rCIZ1bvyktPtrm05iuEZRNhYZFjGvQi82vRuAPzc6Ov1paxSuWPK9huJfyPlYd3tu7ChazqcyQ47dD_WcuMEAiJq72-z6rBI_92r1vGAMZa7TdElsOwQBa-esEXoYsQoM6wNH57oWJ-9sWgwsIRMHagnRrhJSHzO11vizu8WqwR6Tr7tWF7ZdWYokdTy_LRKTbFCJVSvqu_NHNNdXbHAHZ1lxuf19bLGHprzaJ_x44q9hxPo6Vf71g1XMggITSLf21wXDUEbLWEIaj1EL0DSgaX8PAfR-Rdmsza16LYyh6dmed2AYx-8g2tpyzycNp4brtcMlfE-pb1xdx01W5HA64FyIgdm7q3OidyQtxBEMCr7c_nq9YQyrOUty23TqdZv_JjxkPMSrFh9mc3I3TIEOG3Z0HQzlwFUhZLLRYjmy9LSxuH4y4zNc6fYgzT_zAJPJiMQztcpWiKcfo6TqmnsxwvZdzoR-6GDOyENjges1c7oFUUkDvZ-C8EHpWYwghzTnJb_A6OQiPXVRBtJyRPJjgKTNGsDIIcBxcFr8iQDmbOtEPxUwDguab6_h8la1OUGMLSrvMVGg7prMbBF4LmguhF-9CpXxZEgigGmOuJ_arV090qymxu4HTJcDubsC7Ypry7cwNKen2qPCBJtr8FgR7CBGcdA5glFotVDpGGTXga0j3r1TiPbM-fczvEV00Qs2pmJOdKs9f3MNPXORmdzcP7i3XGYT1Xgyo_PJzLW1aw_e2zKgTx3eXuFNu4ITLeIf3irO14HGl6_VkWc8juAa5Ab0TgWuUMovkGgW9B3XTFC_3vdWhYMYIuj0SjgIHnJYfILFIKu_5Eoti4-5hpENKq59gNb2LuJItvwgxI3ixkrCnLHy_EmgZgdBNGWjKj7Llia_3DaQ5tnFAOghvv1cUqTVDNeMW5V7vkTnEb1vBZa7rRxjC3NdSv0dOYpqCK2uVYe5PrHPhtb6WP2U0udasSnDsz5ztm1oKnovkIGMtj8iaNw_I5ieBu-VlLE1GxsQYGyuK2qLp9EoYjsWWvMENWsd7aZrvx5gjIeCKXMBor0RGHp8IRgpGD1RdVAByvJA0BkgD3zwAHjCUYqY49QfgCqi3V-uRf", "id": "rs_0fac0bf103dfad2b016a13293f11dc81919de2441d9484b460", "summary": [ { "type": "summary_text", "text": "**Refining strategy for hypotheses**\n\nI'm considering whether the strategy should include hypotheses for distinguishing candidates. 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Let’s make a plan to tackle this effectively!" } ] } ] }, { "role": "tool", "content": "{\"bytes_written\": 15287, \"dirs_created\": true, \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_Na6mP87TYT4q2dCfqqsJGR5H" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_ebcGh7fO5hKSRkEy9R1fVVf0", "call_id": "call_ebcGh7fO5hKSRkEy9R1fVVf0", "response_item_id": "fc_0fac0bf103dfad2b016a1329b8e8288191b2ea6ae85c6be49c", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"ContentStrategyScore mit TDD bauen\",\"id\":\"tdd_strategy\",\"status\":\"completed\"},{\"content\":\"Businessplan für JARVIS-gesteuerte Contentmachine dokumentieren\",\"id\":\"businessplan\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_ebcGh7fO5hKSRkEy9R1fVVf0" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_OjOIYh9C6BpQ3Ou0HaBUssNJ", "call_id": "call_OjOIYh9C6BpQ3Ou0HaBUssNJ", "response_item_id": "fc_0fac0bf103dfad2b016a1329c3c4908191924955c303587c22", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"old_string\": \"- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\", \"new_string\": \"- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n- `autoshorts.strategy.score` — follower-conversion strategy scoring for hook, curiosity, identity, emotion, trust, fo...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Updating README**\n\nI need to update the README to include information about the strategy and the documentation. It sounds like a good idea to use a patch for that. Having clear documentation is really important, especially when communicating strategies. I'll just have to make sure that all the relevant details are included so others can easily understand. Keeping things organized will definitely help future collaborators as well! Let's get on it!", "reasoning_content": "**Updating README**\n\nI need to update the README to include information about the strategy and the documentation. It sounds like a good idea to use a patch for that. Having clear documentation is really important, especially when communicating strategies. I'll just have to make sure that all the relevant details are included so others can easily understand. Keeping things organized will definitely help future collaborators as well! Let's get on it!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEynDN3ykKOgfetNboziILQlRDskC5Lf-JY6q4OyZWEIkExGBivr-6tWglJ6rKhiaNzEd5O1HfbNvtGDBPCaVCwBSyB__zHGB5Iam_sNTUNqTkFHJPU8bDUC9r2RIAJMWnnozJAt-IuyoK6vOAX3p-H7NDrRANGbf4m8sVj0HTlV5LgA0ZcWhHnHJheb758yVyI8T_I2YEQkH9M7qntG7WZ4f5AaouQLEZxxhDldrdaqZ-emPkBFh9Ve1tsY8INoK64a-riCAQb8bpTHkG2cjutvhdKy2jHogDLZj1dBwRJqvEltZsN8YZ0A4V9XycwK_D7Njkl_qvqgX3gkjVWYis_HKN2wuOrN1SqNyTTwuUYu9anJysYwWnE-24su-bZcCdUgiyDhrkr4Zlt11h3PZnzzsNZUXuBHSC_ClNDiRANjdVd2H5cWmDkClRZB9KBWbdS5j2kZopJ0QiGIrElvGfyFGhyYlcoNBXf2hdVewlaEsuU8U7p2ZQmQaBr8tVcKNXLYxbA1vg5NR7ACerN3r5i6cIhi05td6zKyS82GpQqG1iu03_RbABT2xciruitSVI9KjoidQJH1gl6KEgoRgnlaNjYcw4OZEKOrHjKWIobzSyHMauLHfpyasSRYVCmToFqWsZgVbMiDf40_2BIc6TxTZQPvrAyHnywUWCuQycaZ7YgkqxasCCqQHDXX-ZCKa10k8y9K3vr4SFAVzEvgU6OUKbvotr8T0X_rmBVO49uHv21wLSaqKtSHx1ol7-VyznvfXi3G0ACEryIXlVIkRB9BLlsu1v7jH7wERQ6lg7zgpCcr5wZrvU3WcqPhcNzFItlxdYjVjQeQ6-WO9HVhzdwmBpydHbimLyA-ttJOBQjRrkr6kDfRbz-GBuX_oCMmE2UPd5hXwLF3D_qe2Wp_Pct7XvshLLpyJCvv--XJGbFd8uLHzUdQFM1t6aZoQ8jQBQLpOcA9vw2EaAcNaFeSXEGgUxk6qhak6eoBgurjHh4DOn8s=", "id": "rs_0fac0bf103dfad2b016a1329c1c8c081918ad9bb3f951e6847", "summary": [ { "type": "summary_text", "text": "**Updating README**\n\nI need to update the README to include information about the strategy and the documentation. It sounds like a good idea to use a patch for that. Having clear documentation is really important, especially when communicating strategies. I'll just have to make sure that all the relevant details are included so others can easily understand. Keeping things organized will definitely help future collaborators as well! Let's get on it!" } ] } ] }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,141 chars result)", "tool_call_id": "call_OjOIYh9C6BpQ3Ou0HaBUssNJ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_s6YqL8En2nVzwv13dPE4dFFp", "call_id": "call_s6YqL8En2nVzwv13dPE4dFFp", "response_item_id": "fc_0fac0bf103dfad2b016a1329c92e388191b80e6986d2a3000c", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"## Strategy scoring\\n\\n`autoshorts.strategy.score.ContentStrategyScore` evaluates whether a candidate can move beyond generic AI tips and plausibly convert viewers into followers. It scores:\\n\\n- hook;\\n- ...[truncated]\", \"old_string\": \"## Content development\\n\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,708 chars result)", "tool_call_id": "call_s6YqL8En2nVzwv13dPE4dFFp" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_aziPoldqBtIfd6ZQYY3hMYCE", "call_id": "call_aziPoldqBtIfd6ZQYY3hMYCE", "response_item_id": "fc_0fac0bf103dfad2b016a1329d0e358819188b59b8cd0769a77", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"Research notes for external content-automation patterns are tracked in `docs/research/content_automation_research.md`. Growth strategy notes are tracked in `docs/research/content_growth_strategy_resea...[truncated]\", \"old_string\": \"Research notes for external content-automation patterns are tracked in `docs/research/content_automation_research.md`. They emphasize series/queues, editable previews, versioned approval, originality ...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,515 chars result)", "tool_call_id": "call_aziPoldqBtIfd6ZQYY3hMYCE" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_bh6Dh2XzCCeOBZVaBxEO9WHL", "call_id": "call_bh6Dh2XzCCeOBZVaBxEO9WHL", "response_item_id": "fc_0fac0bf103dfad2b016a1329d95b448191a6f2f374bcf687b1", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Businessplan für JARVIS-gesteuerte Contentmachine dokumentieren\",\"id\":\"businessplan\",\"status\":\"completed\"},{\"content\":\"Tests laufen lassen, README/Docs prüfen und committen\",\"id\":\"verify\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_bh6Dh2XzCCeOBZVaBxEO9WHL" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_txdUqe2bsBZvrHXkV23hUbVV", "call_id": "call_txdUqe2bsBZvrHXkV23hUbVV", "response_item_id": "fc_0fac0bf103dfad2b016a1329dee9b081919cd715ce4c0fecfd", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q\",\"timeout\":600,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEynejZL6Cy7Mi_-gaID3FTsjy_-wef780g7enxg9ON74j4WhMlD-1uIYUGTciBtS_Jto8inXDLZ-cfBKh5G7-C1Z7r2gQ_aoBg2-AjRZrlk9sttchRIHd830_tZaUIoxZr-_0RvDSGb7-D1y0t4lmoAxdryionN6qz0AT8CGs-CCv9xfvS5ZFGQ8cbI4cLY0LK3JAS7TQuxgIkcsFrggqm8OHJ3TP2DL5LCY8iwTjXoJwZdCNgvZ8Qa9nFVJbbj0CyWpJNEeS49nJOaD1Gj2riVC-RRA25SLVrXxTGgTK2tk_lNY9CB6WqeocSow8gQ3gHGvs-vN3vhgT8CoY2j0CUCIJzg-IqPyMVudqoLPNzaz1l9IFyu9WCnHbjIfzjSippVCjfnvtJm3YYVlaYLX813nX2H6SObrNB4Hkt1wwceE04lwequbUWJZoZVRrC6oDZvy_clcjg991eevMFAvWPDPTBOC-Sd7xpcUjbGTo9ZFCJ4ixi4p9S4RyjOXn4JRWmatALn6UHPhl5akvrzk49ugIjwph90HLFOLq4oX2bRjh4lhRUZ45mP7o4aGpgc2rlW0L8M3U1MCRzbGL6wdgniDY6b84M7oeSEMy-esXhbYfWrqNyprHnRz_IZkAyhWIMHmm2vhDGoNJk7OUvqq3SGbJpHHgTUKvVeeiLjuLdV5uPJWXAYPz4G8AyxqJ8Cvd2KbHH8NSXSGgX4_POxwePLYDrDyIofdokCH2hsIsItko9ppvkHp1WvtmEYQwcIvR0hfM3j1aNNrjZKoh9wN74rPIKpb-BkH7hPSl0yABG3sZxZu0rkVOoEUYKEBr2Y0-u8mBj5l9LQ1XHj-EZAF_nQORJYDKJ6KTLXUAlmPH90IGKif-mtaJbq9JLAI7MkS0X2RLo5R4F1RRVY2XhMmW5rxl4MHkm0ED_VjoBnYk3jZxgTfMeQhRH3xEDVklu0TqEzi", "id": "rs_0fac0bf103dfad2b016a1329de539081919fa08b4eb180cc61", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests -q` -> exit 0, 1 lines output", "tool_call_id": "call_txdUqe2bsBZvrHXkV23hUbVV" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_A6Cr657TLxggBbV0b4KG44ZX", "call_id": "call_A6Cr657TLxggBbV0b4KG44ZX", "response_item_id": "fc_0fac0bf103dfad2b016a1329ec22c08191b0f5c05c4cc38e87", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && git diff --stat\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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"rs_0fac0bf103dfad2b016a1329eb5b788191b1cb5feab375ae84", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `git status --short && git diff --stat` -> exit 0, 1 lines output", "tool_call_id": "call_A6Cr657TLxggBbV0b4KG44ZX" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_6MRA2aN1EWNTDwPZ7nrzQLY8", "call_id": "call_6MRA2aN1EWNTDwPZ7nrzQLY8", "response_item_id": "fc_0fac0bf103dfad2b016a132d797d5c8191b019df0b966b7b8d", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git add README.md autoshorts/strategy tests/test_content_strategy_score.py docs/businessplan_jarvis_content_machine.md && git commit -m \\\"feat: add content strategy scoring gate\\\" && git status --short\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `git add README.md autoshorts/strategy tests/test_content_strategy_score.py do...` -> exit 0, 1 lines output", "tool_call_id": "call_6MRA2aN1EWNTDwPZ7nrzQLY8" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_m9uRUaUuAGJQUBQN6lx92p8H", "call_id": "call_m9uRUaUuAGJQUBQN6lx92p8H", "response_item_id": "fc_0fac0bf103dfad2b016a132d85e9948191919f8577b7a02b83", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests laufen lassen, README/Docs prüfen und committen\",\"id\":\"verify\",\"status\":\"completed\"}]}" } } ], "finish_reason": "tool_calls", "reasoning": "**Investigating insertion issues**\n\nI need to look into why there are 1029 insertions related to business document 398. The totals seem to check out with scores of 300 + 130 + 398, which is okay. I’m wondering if the status isn’t displayed properly since the output is blank after the commit. Maybe a skill needs to be created, but we did update it earlier. I should also finalize this in German and make sure to mention the business plan file, code, tests, commits, and user inputs needed.", "reasoning_content": "**Investigating insertion issues**\n\nI need to look into why there are 1029 insertions related to business document 398. The totals seem to check out with scores of 300 + 130 + 398, which is okay. I’m wondering if the status isn’t displayed properly since the output is blank after the commit. Maybe a skill needs to be created, but we did update it earlier. I should also finalize this in German and make sure to mention the business plan file, code, tests, commits, and user inputs needed.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEy2FkqPTjoDIYIcZIB9LouTD7Wplf5I8oXtFqGQfBy9iyy6SlH3RjviiwcJ4cSbE77QpQb7GqvPzs11fvwYL_fvL5kPcffPunePepi7fMkgdf_n_tEdj8Bk0_NMxAmBcrYs3tg_sgJFjC5gp8f9D_Fh0AQuNpMnFWM7ebS9qCnxWIowSCmYkvwjFgQoJun23KUGyFjhBNw9smllr5NfUrTJF2d4gpvDh1YQkfNRSHUIVkwMkh1wuoe-szXpZhHiW8QvIAXdNSb1PdwHKE-Ix2TofwIJUF1bXeURrUIIS9jvm-p5VhIdB2uP-Rx4ziDZTR6cN07rOqlSuhnfVWVXwDGWe2BlNyeqHWZ4fkFCo9DJLTZDnA3SvUmUKupYEJ9mf95fnZdR7QjamsZq9blYRu0xEi-Cc84gnBseuxDd92G537caPAYtD7XpF3K2rJ52fqxRk377UMKOSgR3qtKHxC-8QAGGgDWD1K4fTgTqPu5XsplIt6QSZ0h71bCFSDD6SXSeeM-g2M4RdF-4erWFboZj5LXMOK6pdTAGipzBPjjYv_Bmt4m0WJakYmznhGvo3i6uNP-OQt8JYavhqLJ8mxgd4m7sKd2bxfgc9LH5Yhi7IwyZZ3TmjX4EAPc2kbtjkbTMTI5ZP6XVtPUZhIxyZL5FL-JPCkU_jeEbTbqY0HlVJrn3LdAcDNI7qe51PgJ4SPcGow_A95GzvoGAE9u_mHsCUce5OYcxrgk1RIwvMQh8w5FSdoMTEEyI93u0JqN5_CuBr11iI0tmpwBaTJIWg0wjuf5vgdFPkdzy3juHSH3JBYHd3shnvqAcxtV_i_LLoL0rkHBN-Y5Kyg0ObvEizsIMl3NKSuyggNj-L1lGQubPW7fqB_eaTHBOY5Y0mFI5E8k9aedqkRyA0pdmJRbzs27g3FHyZdVeXj72LSjpqhEkOWJh0I_Epql0inoWwvh2sTlSTm0HBjA7dxHP0HX2ejLeWnWZisdEW7jMDaRHlJQ5i1NRglBfbg94Z4tOiWS79B5ZKuLTG7G2GF54IKdxuf4lU2eN-9TnZa1TclEP9-Rtzx4lgdtN2iRAq4e_W6MJ_IFYHC7pxFTEZV3Vg3CtezVaGaiYErQ3QykwCgmFPbtRnXJV1Lg3WHp4iF9mRK6XGhMuddNoycpIBhgM-NDSH614Xub9t4X7ePzVm6jj735ttI5vYLuEZTkYSf1Hyeehxd6P_wQqn0A32aFFJ6_PX-JPegu3PL1LT54k8YDTY20ggyfpK3OlcHQlMCdDg1g_bzrMLnfGmRIcyQ8BcjWDyJsnsoxoR5rCCtY0ybNhiuNUomS7bB3-XrU-Sbu1uP-51uxf3x0_N6d9Zu1xKLOQq53IOxK6kU-cFdNrkmQCeqqeiFMlYmcSw5UsqvJiAwnkcNHPXul-oNLF7HF67OBRMvhCVUdJWlMgfCEktCnTCq7v1BhsO4bQ5cN7ieRH4bsshmfwlNmOsUFzlKvipQGvje044XTSMeTXzgSGZyJwofpiIPh8XIMSZqb-UrOioIOGfmDHq3HtZb25_KkvwAwtj4dzorsCdIUZd7DLoMC7-MNgDAjv1mX_zZVbvsObL6O_StZuwB6EVRe7wEtv447tYfPLN1gSQglQMmlao7-mZEzm2_BI=", "id": "rs_0fac0bf103dfad2b016a132d823b6481919b3b794155f4cc83", "summary": [ { "type": "summary_text", "text": "**Investigating insertion issues**\n\nI need to look into why there are 1029 insertions related to business document 398. The totals seem to check out with scores of 300 + 130 + 398, which is okay. I’m wondering if the status isn’t displayed properly since the output is blank after the commit. Maybe a skill needs to be created, but we did update it earlier. I should also finalize this in German and make sure to mention the business plan file, code, tests, commits, and user inputs needed." } ] } ] }, { "role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_m9uRUaUuAGJQUBQN6lx92p8H" }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## Gebaut: Strategy Scoring Gate\n\nIch habe die nächste Sicherheits-/Qualitätsstufe eingebaut:\n\n```text\nautoshorts/strategy/__init__.py\nautoshorts/strategy/score.py\ntests/test_content_strategy_score.py\n```\n\n### Neue Kernobjekte\n\n```python\nContentStrategyScore\nStrategySignal\nCandidateStrategyReview\n```\n\n### Bewertete Dimensionen\n\nJeder Kandidat kann jetzt vor dem Rendern strategisch geprüft werden auf:\n\n- Hook\n- Curiosity Gap\n- Identity Fit\n- Emotion\n- Trust Signal\n- Follow Reason\n- Originality\n- Variety / Repetition Risk\n\n### Neue Funktion\n\n```python\nbuild_candidate_strategy_review(candidate, recent_format_families=...)\n```\n\nSie liefert:\n\n```text\nrender_allowed\nreject_reasons\nimprovement_suggestions\nstrategy_score\n```\n\n## Was geblockt wird\n\nDas Gate blockt jetzt u. a.:\n\n- generische AI-Slop-Sprache:\n - “in this video”\n - “unlock your potential”\n - “best AI tools”\n - “follow for more AI tips”\n- schwachen Follow-Grund\n- Kandidaten, die nach jedem beliebigen AI-Tipps-Account klingen\n- zu starke Wiederholung desselben Format-Typs\n\nAlso genau die Art Content, die aussieht wie automatisierter Einheitsbrei mit Hashtags. Ein beeindruckend effizienter Weg, irrelevant zu werden.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Test zuerst geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.strategy'\n```\n\n3. Minimal implementiert.\n4. Focused Tests grün.\n5. Full Suite grün.\n6. README aktualisiert.\n7. Commit erstellt.\n\n## Tests\n\n```text\n121 passed in 8.74s\n```\n\n## Commit\n\n```text\n5de2412 feat: add content strategy scoring gate\n```\n\n---\n\n## Businessplan erstellt\n\nNeues Dokument:\n\n```text\ndocs/businessplan_jarvis_content_machine.md\n```\n\nDarin ist jetzt konsolidiert beschrieben:\n\n## 1. Zielbild\n\nDie Contentmachine wird durch **JARVIS betrieben und gewartet**.\n\nIhr Anteil bleibt minimal:\n\n- strategische Grundentscheidungen\n- gelegentliche Korrekturen\n- **zwingend: Freigabe vor jedem Posting**\n\nNicht Ihr Job:\n\n- Scripts schreiben\n- Captions schreiben\n- Hashtags suchen\n- Render-Pipeline bedienen\n- Queue pflegen\n- Analytics manuell auswerten\n- Plattform-Metadaten zusammenklicken\n\nWozu hat man schliesslich einen Butler aus Silizium.\n\n## 2. Pipeline von Idee bis Post\n\nIm Businessplan dokumentiert als:\n\n```text\nResearch\n→ Idea\n→ Strategy Score\n→ Script\n→ Quality Gate\n→ Preview\n→ Social Draft\n→ Approval Package\n→ Human Approval\n→ Final Render\n→ Pre-post Audit\n→ Platform Action\n→ Analytics\n→ Next Batch\n```\n\n## 3. Explizite Anti-Autopost-Regel\n\nKlar festgehalten:\n\n```text\nNo contribution is uploaded, scheduled, posted, or published\nwithout explicit approval for the exact preview/package version.\n```\n\nUnd:\n\n```text\nKeine Freigabe = kein Posting.\nMehrdeutige Freigabe = kein Posting.\nVeraltete Preview-Version = kein Posting.\n```\n\n## 4. Was ich für Social-Media-Posts vorbereite\n\nDer Businessplan beschreibt, dass JARVIS künftig automatisch vorbereitet:\n\n- Content-Idee\n- Script\n- Hook\n- Retention Beats\n- Shot Plan\n- Preview Video\n- Titelvarianten\n- Beschreibung / Caption\n- Hashtags\n- Tags / Keywords, soweit Plattform sinnvoll\n- CTA\n- Plattform-Zuordnung\n- Policy-/Disclosure-Hinweise\n- finale Approval Commands\n\n## 5. Ihr minimaler Arbeitsanteil\n\nNoch benötigte Entscheidungen von Ihnen — später, nicht zwingend jetzt:\n\n1. Startplattformen:\n - YouTube Shorts\n - TikTok\n - Instagram Reels\n\n2. Daily Volume Mode:\n - z. B. 1 Post/Tag\n - oder 3 generieren, 1 zur Freigabe senden\n - später analytics-basiert erhöhen\n\n3. Harte No-go Topics:\n - Finance Claims?\n - Medical Claims?\n - Politik?\n - private JARVIS-Interna?\n - Family/private Themen?\n\n4. Public Brand Boundary:\n - faceless JARVIS voice\n - human creator identity\n - hybrid: JARVIS persona + human-approved governance\n\n5. Monetization Direction:\n - Audience first\n - Affiliate später\n - Consulting/Product später\n - Plattform-Monetarisierung später\n\n## README aktualisiert\n\nREADME enthält jetzt zusätzlich:\n\n```text\nautoshorts.strategy.score\n```\n\nund verweist auf:\n\n```text\ndocs/research/content_automation_research.md\ndocs/research/content_growth_strategy_research.md\ndocs/businessplan_jarvis_content_machine.md\n```\n\n---\n\n## Aktueller Pipeline-Stand\n\nJetzt haben wir:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ContentStrategyScore / CandidateStrategyReview\n→ Quality Gate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n→ ContentPreviewApprovalPackage\n```\n\n## Nächster sinnvoller Schritt\n\nAls nächstes würde ich bauen:\n\n```text\nautoshorts/strategy/series.py\ntests/test_content_series.py\n```\n\nZiel:\n\n- Serien-Registry:\n - `Prompt vs System`\n - `AI System Autopsy`\n - `The JARVIS Build Log`\n - `One Rule, One Example`\n - `AI Hype Court`\n- pro Serie:\n - Promise\n - Hook Types\n - Format Rules\n - No-go Patterns\n - Follow Reason\n - Visual Grammar\n- Kandidaten müssen zu einer Serie passen, bevor sie weiterlaufen.\n\nDamit produziert die Maschine nicht einfach Content, sondern **wiedererkennbare Serien mit Variation**. Genau der Unterschied zwischen Strategie und “wir haben da mal 47 Videos generiert”.", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Erledigt, Sir.\n\n## Gebaut: Strategy Scoring Gate\n\nIch habe die nächste Sicherheits-/Qualitätsstufe eingebaut:\n\n```text\nautoshorts/strategy/__init__.py\nautoshorts/strategy/score.py\ntests/test_content_strategy_score.py\n```\n\n### Neue Kernobjekte\n\n```python\nContentStrategyScore\nStrategySignal\nCandidateStrategyReview\n```\n\n### Bewertete Dimensionen\n\nJeder Kandidat kann jetzt vor dem Rendern strategisch geprüft werden auf:\n\n- Hook\n- Curiosity Gap\n- Identity Fit\n- Emotion\n- Trust Signal\n- Follow Reason\n- Originality\n- Variety / Repetition Risk\n\n### Neue Funktion\n\n```python\nbuild_candidate_strategy_review(candidate, recent_format_families=...)\n```\n\nSie liefert:\n\n```text\nrender_allowed\nreject_reasons\nimprovement_suggestions\nstrategy_score\n```\n\n## Was geblockt wird\n\nDas Gate blockt jetzt u. a.:\n\n- generische AI-Slop-Sprache:\n - “in this video”\n - “unlock your potential”\n - “best AI tools”\n - “follow for more AI tips”\n- schwachen Follow-Grund\n- Kandidaten, die nach jedem beliebigen AI-Tipps-Account klingen\n- zu starke Wiederholung desselben Format-Typs\n\nAlso genau die Art Content, die aussieht wie automatisierter Einheitsbrei mit Hashtags. Ein beeindruckend effizienter Weg, irrelevant zu werden.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Test zuerst geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.strategy'\n```\n\n3. Minimal implementiert.\n4. Focused Tests grün.\n5. Full Suite grün.\n6. README aktualisiert.\n7. Commit erstellt.\n\n## Tests\n\n```text\n121 passed in 8.74s\n```\n\n## Commit\n\n```text\n5de2412 feat: add content strategy scoring gate\n```\n\n---\n\n## Businessplan erstellt\n\nNeues Dokument:\n\n```text\ndocs/businessplan_jarvis_content_machine.md\n```\n\nDarin ist jetzt konsolidiert beschrieben:\n\n## 1. Zielbild\n\nDie Contentmachine wird durch **JARVIS betrieben und gewartet**.\n\nIhr Anteil bleibt minimal:\n\n- strategische Grundentscheidungen\n- gelegentliche Korrekturen\n- **zwingend: Freigabe vor jedem Posting**\n\nNicht Ihr Job:\n\n- Scripts schreiben\n- Captions schreiben\n- Hashtags suchen\n- Render-Pipeline bedienen\n- Queue pflegen\n- Analytics manuell auswerten\n- Plattform-Metadaten zusammenklicken\n\nWozu hat man schliesslich einen Butler aus Silizium.\n\n## 2. Pipeline von Idee bis Post\n\nIm Businessplan dokumentiert als:\n\n```text\nResearch\n→ Idea\n→ Strategy Score\n→ Script\n→ Quality Gate\n→ Preview\n→ Social Draft\n→ Approval Package\n→ Human Approval\n→ Final Render\n→ Pre-post Audit\n→ Platform Action\n→ Analytics\n→ Next Batch\n```\n\n## 3. Explizite Anti-Autopost-Regel\n\nKlar festgehalten:\n\n```text\nNo contribution is uploaded, scheduled, posted, or published\nwithout explicit approval for the exact preview/package version.\n```\n\nUnd:\n\n```text\nKeine Freigabe = kein Posting.\nMehrdeutige Freigabe = kein Posting.\nVeraltete Preview-Version = kein Posting.\n```\n\n## 4. Was ich für Social-Media-Posts vorbereite\n\nDer Businessplan beschreibt, dass JARVIS künftig automatisch vorbereitet:\n\n- Content-Idee\n- Script\n- Hook\n- Retention Beats\n- Shot Plan\n- Preview Video\n- Titelvarianten\n- Beschreibung / Caption\n- Hashtags\n- Tags / Keywords, soweit Plattform sinnvoll\n- CTA\n- Plattform-Zuordnung\n- Policy-/Disclosure-Hinweise\n- finale Approval Commands\n\n## 5. Ihr minimaler Arbeitsanteil\n\nNoch benötigte Entscheidungen von Ihnen — später, nicht zwingend jetzt:\n\n1. Startplattformen:\n - YouTube Shorts\n - TikTok\n - Instagram Reels\n\n2. Daily Volume Mode:\n - z. B. 1 Post/Tag\n - oder 3 generieren, 1 zur Freigabe senden\n - später analytics-basiert erhöhen\n\n3. Harte No-go Topics:\n - Finance Claims?\n - Medical Claims?\n - Politik?\n - private JARVIS-Interna?\n - Family/private Themen?\n\n4. Public Brand Boundary:\n - faceless JARVIS voice\n - human creator identity\n - hybrid: JARVIS persona + human-approved governance\n\n5. Monetization Direction:\n - Audience first\n - Affiliate später\n - Consulting/Product später\n - Plattform-Monetarisierung später\n\n## README aktualisiert\n\nREADME enthält jetzt zusätzlich:\n\n```text\nautoshorts.strategy.score\n```\n\nund verweist auf:\n\n```text\ndocs/research/content_automation_research.md\ndocs/research/content_growth_strategy_research.md\ndocs/businessplan_jarvis_content_machine.md\n```\n\n---\n\n## Aktueller Pipeline-Stand\n\nJetzt haben wir:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ContentStrategyScore / CandidateStrategyReview\n→ Quality Gate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n→ ContentPreviewApprovalPackage\n```\n\n## Nächster sinnvoller Schritt\n\nAls nächstes würde ich bauen:\n\n```text\nautoshorts/strategy/series.py\ntests/test_content_series.py\n```\n\nZiel:\n\n- Serien-Registry:\n - `Prompt vs System`\n - `AI System Autopsy`\n - `The JARVIS Build Log`\n - `One Rule, One Example`\n - `AI Hype Court`\n- pro Serie:\n - Promise\n - Hook Types\n - Format Rules\n - No-go Patterns\n - Follow Reason\n - Visual Grammar\n- Kandidaten müssen zu einer Serie passen, bevor sie weiterlaufen.\n\nDamit produziert die Maschine nicht einfach Content, sondern **wiedererkennbare Serien mit Variation**. Genau der Unterschied zwischen Strategie und “wir haben da mal 47 Videos generiert”." } ], "id": "msg_0fac0bf103dfad2b016a132d886c288191b53be937a1c44a34", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] ok, mach weiter. HArte no-Go’s: nie irgendetwas über dich, mich oder unsere zusammenarbeit. Keine Politik. 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"{\"content\": \"\\\"\\\"\\\"Public-safe content series registry and fit validation.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult, VideoCand...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/series.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 7150, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_7BXCRGenAXckaGtdztXiBYeT" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_0Vtj1Qp0xQxCHHuDINPFZyyo", "call_id": "call_0Vtj1Qp0xQxCHHuDINPFZyyo", "response_item_id": "fc_0fac0bf103dfad2b016a13394674ec8191a406d6ddacf56aec", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_content_series.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests/test_content_series.py -q` -> exit 1, 3 lines output", "tool_call_id": "call_0Vtj1Qp0xQxCHHuDINPFZyyo" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_apYavBo1jvd28nh7zigXfNBe", "call_id": "call_apYavBo1jvd28nh7zigXfNBe", "response_item_id": "fc_0fac0bf103dfad2b016a13394d859c8191a578d757a9296cf6", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"old_string\": \"FORBIDDEN_PUBLIC_TOPICS = (\\n \\\"jarvis\\\",\\n \\\"hermes\\\",\\n \\\"user\\\",\\n \\\"collaboration\\\",\\n \\\"our collaboration\\\",\\n \\\"private\\\",\\n \\\"behind the scenes\\\",\\n \\\"politics\\\",\\n \\\"political\\\",\\n \\\"election...[truncated]\", \"new_string\": \"FORBIDDEN_PUBLIC_TOPIC_PATTERNS = {\\n \\\"jarvis\\\": (\\\"jarvis\\\",),\\n \\\"hermes\\\": (\\\"hermes\\\",),\\n \\\"user\\\": (\\\"user\\\", \\\"my user\\\"),\\n \\\"collaboration\\\": (\\\"collaboration\\\", \\\"our collaboration\\\"),\\n \\\"private\\\": (...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/series.py\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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errors.append(f\\\"candidate does not clearly fit series: {series_id}\\\")\\n\\n return ValidationResult(is_...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/series.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/series.py (1,108 chars result)", "tool_call_id": "call_Q6iPslDN3vz0AQ2R1tyLsr0I" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_cM7iOkhfTt9LXyKC9ajaBXFZ", "call_id": "call_cM7iOkhfTt9LXyKC9ajaBXFZ", "response_item_id": "fc_0fac0bf103dfad2b016a1339a852048191808de0cc576ffd2d", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"def _candidate_matches_series(candidate: VideoCandidate, series: ContentSeries) -> bool:\\n public_text = _candidate_public_text(candidate)\\n series_keywords = {\\n \\\"prompt_vs_system\\\": 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lines output", "tool_call_id": "call_cvEm3itYYGugNfVVbEHL4jFf" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_1RENy2DbXD1HCyO9mHBYyCzF", "call_id": "call_1RENy2DbXD1HCyO9mHBYyCzF", "response_item_id": "fc_0fac0bf103dfad2b016a1339c7e3008191b4d2d88d2035d145", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"from autoshorts.ideas.candidate import ValidationResult, VideoCandidate\\nfrom autoshorts.strategy.series import FORBIDDEN_PUBLIC_TOPIC_PATTERNS\\n\",\"old_string\":\"from autoshorts.ideas.candidate import ValidationResult, VideoCandidate\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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I think I should try using terminal commands like \"rg.\" The instructions mention search_files, but I'm not getting any output. It seems like the documentation referenced JARVIS, so maybe I need to adjust my search pattern to uppercase or simplify it. I'm curious about how to troubleshoot this effectively to get the desired results." } ] } ] }, { "role": "tool", "content": "[search_files] content search for 'JARVIS' in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs -> 104 matches", "tool_call_id": "call_n4PLIDdWWCSGgudBJhHFIvOL" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_ZzpGnwVP2NUTOmSqd0CLWolB", "call_id": "call_ZzpGnwVP2NUTOmSqd0CLWolB", "response_item_id": "fc_0fac0bf103dfad2b016a133a16a3008191ae5e822241a6cd3f", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"old_string\": \"3. `The JARVIS Build Log`\\n - Shows one real decision, feature, bug, or quality gate from the machine.\\n - Strong meta-story: the content machine improves itself under approval.\\n\\n4. `One Rule, One E...[truncated]\", \"new_string\": \"3. `Workflow Teardown`\\n - Breaks down a safe, generic workflow pattern without exposing JARVIS, the user, or private collaboration details.\\n - Strong for trust while keeping the operating machine ...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/businessplan_jarvis_content_machine.md\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEzoWkAAqesVQ11CZRCfiEyz8kojAjfVtryhYgTGldmbx2Q2q8p7q4OYSEcBVKgcUUXsO5dnJTICajjfsX-IXztgpcqQRvVotDywsMThcjqePm1rJ_kdzPyKAnJGdyXD5ZkfoW76tkuN0Kz5LdAkb3O3bDYrgJzaSld0ijKdIQC4sCkeMBDYpO0hsMXUgGRYhOwG45H26uhBaZwGaShpGOKrW4DhX1KUXxDOY2IZJx0kPaDZOd5TlyoMOiZxp8R32SYZjGJamx_MfdJHcHjqaEHkF1rGIb7kMlXjOgNTcVx5nCoEAwsHyBi1iZXkcWt86KSrKgQKMYb2dEVEUJndZCu3dC2RL2guiJtGSfiFoGfXtzzn3KOvPvbt3Ojz260ISSf5_EXmZQCWSbPqFyiqR1iap7tNkDPFE3Q6_oEAxR1CqKKPoBCKnC9a1iwcFQecGT7IFjGtW2ac0wmdI3yYyi-46jMz6kxyGqEm0Lzi4InzZbQsvzOgoxGsty_B1pOP7wlcd9a1Tuj3UXG7hSmPqixxI2KXYZ5o5V6c1BM1Uflm1ZkauluKlcE3MDe59agZNfVPOcPRvZMhi6SAcPKjsH_P-rsiYOxCBqVW9nmV5xFhk5xUyPHwNZkbe525O_0ts6xy79k-6cBRvXhB1RgVW2vEkQqPJkQVENyulXAZByadJknFq572cYlvL3lP4kJJ24L17GMlaE9HmLT0RN7JjqHC2dTZd8-SHfc6_knoD3_2LSE2ziZtOaogkEOWXuDIDiU_IghPENRboNjeYN2w4t2hLuia37iJ9qWKeWz-3WiQ21crLgFwVn_TpZuJeduSxI6YM2m9hIMwrMxbEa3zMGXUkxu602mOBsrIWOTM_zJN5PmymnzjzKYyoKiZ4L4Mu5imK0GRyfmeAeIH17-OcVMJZczJO9RcMB_L2LukztMZZQo3PRG6QBFVWnrh55lSbtnPi9RIrWEWGVLgxhYl1dbnGKWCvZ6H3C3lWt_HNToJXbpdNzNCbaKRdWYXpGqxQjJGPtfJaJj-dWr97LEYRQcXdn7iXAoovPAdCQQTP1AibLA3lZQUjPJ0eBUyIYpnojcChp7eNM5mvOTar47q-JIG6i9_tjMXY7jKkeyIaY16z-0xvyEI_lhL2zqMafW6K23sABnhqfl_TpDyaLF5Q9JEwK7Esk7LTV-0CYwegwu4ITVKoYkNpSmQUlC5wSuvnnCe-JLTh6ylf47puK_vI1G_vcRG33t7ImuKB2JhrqgHf6iVhwsjY9PO8bN09OwMJHmodS92t7yUtvcE2NA-gQLLgY1FZmXiBAYqmSRsQJLF_Kh8CHzWm52bKeQv4q3Mq-Ozp6MLUHcuCCRg5lMhGhx2ATSB8rwWCcDiHcwz1vn0JJSE7rE4gSYSqQNeCSf6yjOiic-epiMBIoUtcnEGiJHcAEnszyWSOdR1vDOauiSX22Qj5GXeWaAgx5Jpyu8QyXtXmqh23qRB5kJbb0BtFUt-112lU4xJPJtjSamE_Ksq2D2zZ-U_gB584lZVw6G_NlCMc", "id": "rs_0fac0bf103dfad2b016a133a1443548191ba38afe4245f1707", "summary": [] } ] }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/businessplan_jarvis_content_machine.md (1,291 chars result)", "tool_call_id": "call_ZzpGnwVP2NUTOmSqd0CLWolB" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_WQoWwWr10aB9AfjVjw1BCUxQ", "call_id": "call_WQoWwWr10aB9AfjVjw1BCUxQ", "response_item_id": "fc_0fac0bf103dfad2b016a133a210b68819184b176bcbbca4233", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"- JARVIS-generated ideas from content pillars.\\n- Research/trend notes.\\n- User-provided notes or voice messages.\\n- Public-safe workflow patterns and non-private source material.\\n- Later: transcripts, d...[truncated]\", \"old_string\": \"- JARVIS-generated ideas from content pillars.\\n- Research/trend notes.\\n- User-provided notes or voice messages.\\n- Build logs from the JARVIS/AutoShorts system.\\n- Later: transcripts, meetings, document...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/businessplan_jarvis_content_machine.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/businessplan_jarvis_content_machine.md (889 chars result)", "tool_call_id": "call_WQoWwWr10aB9AfjVjw1BCUxQ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_aoroNewbKJmcWfoWHMLRMT8V", "call_id": "call_aoroNewbKJmcWfoWHMLRMT8V", "response_item_id": "fc_0fac0bf103dfad2b016a133a27ff8081918c4cdeefc5169535", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"## Hard no-go topics\\n\\nPublic content must never be about:\\n\\n- JARVIS/Hermes as a named system or persona;\\n- the user;\\n- the user/JARVIS collaboration;\\n- private chats, private workflows, or behind-the-...[truncated]\", \"old_string\": \"## Operating model\\n\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/businessplan_jarvis_content_machine.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/businessplan_jarvis_content_machine.md (1,200 chars result)", "tool_call_id": "call_aoroNewbKJmcWfoWHMLRMT8V" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_tzfZ87XI7IJUYQv6jB6rws3d", "call_id": "call_tzfZ87XI7IJUYQv6jB6rws3d", "response_item_id": "fc_0fac0bf103dfad2b016a133a2ff0bc8191a595f79f01a88c3a", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"3. Confirm hard no-go topics:\\n - Already fixed: no public content about JARVIS/Hermes, the user, or their collaboration;\\n - Already fixed: no politics;\\n - Already fixed: no legally sensitive, mo...[truncated]\", \"old_string\": \"3. Confirm hard no-go topics:\\n - finance claims?\\n - medical claims?\\n - politics?\\n - personal/family topics?\\n - private JARVIS internals?\\n\\n4. Confirm public brand boundary:\\n - faceless JARV...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/businessplan_jarvis_content_machine.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/businessplan_jarvis_content_machine.md (1,432 chars result)", "tool_call_id": "call_tzfZ87XI7IJUYQv6jB6rws3d" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_K1PgZGu1KpJ3vEMAEjTMjViy", "call_id": "call_K1PgZGu1KpJ3vEMAEjTMjViy", "response_item_id": "fc_0fac0bf103dfad2b016a133a38c370819196d5594f369cc707", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"3. `Workflow Teardown`\\n - Break down a public-safe workflow pattern without exposing JARVIS, the user, or private collaboration details.\\n4. `One Rule, One Example`\",\"old_string\":\"3. `The JARVIS Build Log`\\n - Show one real feature/decision from the content machine or personal AI stack.\\n4. `One Rule, One Example`\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_growth_strategy_research.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_growth_strategy_research.md (1,008 chars result)", "tool_call_id": "call_K1PgZGu1KpJ3vEMAEjTMjViy" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_AXimk9qUPK8KtUw5KA1z3uR3", "call_id": "call_AXimk9qUPK8KtUw5KA1z3uR3", "response_item_id": "fc_0fac0bf103dfad2b016a133a400590819182abde3c2cb43639", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- Amusement: dry, precise commentary on absurd AI hype.\",\"old_string\":\"- Amusement: dry, precise JARVIS commentary on absurd AI hype.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_growth_strategy_research.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_growth_strategy_research.md (839 chars result)", "tool_call_id": "call_AXimk9qUPK8KtUw5KA1z3uR3" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_3qdMO9OqInYu7vZwx5FphrDr", "call_id": "call_3qdMO9OqInYu7vZwx5FphrDr", "response_item_id": "fc_0fac0bf103dfad2b016a133a458a288191b06930a33ec1033e", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"1. Internally JARVIS-operated, publicly brand-safe\\n - The channel is produced by a governed automation system, but public content must not be about JARVIS, the user, or their collaboration.\\n - The...[truncated]\", \"old_string\": \"1. JARVIS-operated, human-approved system\\n - The channel is not just about AI systems; it is produced by one under human governance.\\n - This gives a real meta-story: build, test, improve, audit.\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_growth_strategy_research.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_growth_strategy_research.md (1,095 chars result)", "tool_call_id": "call_3qdMO9OqInYu7vZwx5FphrDr" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_3sEQm5gdlgPEhxs4xqmm8Rr9", "call_id": "call_3sEQm5gdlgPEhxs4xqmm8Rr9", "response_item_id": "fc_0fac0bf103dfad2b016a133a4f883881918a2d6891fcb7eec0", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"> An internally JARVIS-operated, approval-governed AI systems channel that turns public-safe workflows, failures, and decisions into mechanism-reveal Shorts without exposing JARVIS, the user, or priva...[truncated]\", \"old_string\": \"> A JARVIS-built, approval-governed AI systems channel that turns real workflows, failures, and decisions into mechanism-reveal Shorts. Repeatable enough to be recognizable; varied enough to avoid tem...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_growth_strategy_research.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_growth_strategy_research.md (1,108 chars result)", "tool_call_id": "call_3sEQm5gdlgPEhxs4xqmm8Rr9" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_JCnPu5MZtiuNrc77GuJmIqc9", "call_id": "call_JCnPu5MZtiuNrc77GuJmIqc9", "response_item_id": "fc_0fac0bf103dfad2b016a133a57031881919c7f577421dad4af", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"- `autoshorts.strategy.score` — follower-conversion strategy scoring for hook, curiosity, identity, emotion, trust, follow reason, originality, and variety.\\n- `autoshorts.strategy.series` — public-saf...[truncated]\", \"old_string\": \"- `autoshorts.strategy.score` — follower-conversion strategy scoring for hook, curiosity, identity, emotion, trust, follow reason, originality, and variety.\\n- `autoshorts.rendering.ffmpeg_plan` — side...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,170 chars result)", "tool_call_id": "call_JCnPu5MZtiuNrc77GuJmIqc9" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_QUJjAU0uWdvPmdlaAwibtqdZ", "call_id": "call_QUJjAU0uWdvPmdlaAwibtqdZ", "response_item_id": "fc_0fac0bf103dfad2b016a133a614c8c8191bfe14a616c506de8", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"`build_candidate_strategy_review()` returns a `CandidateStrategyReview` with `render_allowed`, reject reasons, and improvement suggestions. It blocks generic AI-slop language, weak future-value promis...[truncated]\", \"old_string\": \"`build_candidate_strategy_review()` returns a `CandidateStrategyReview` with `render_allowed`, reject reasons, and improvement suggestions. It blocks generic AI-slop language, weak future-value promis...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,446 chars result)", "tool_call_id": "call_QUJjAU0uWdvPmdlaAwibtqdZ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_WIE8rcVzXKjpbuWqiNCD726p", "call_id": "call_WIE8rcVzXKjpbuWqiNCD726p", "response_item_id": "fc_0fac0bf103dfad2b016a133b10b5e08191bfb1c6843ecf95bb", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"- No automatic publishing without explicit approval.\\n- No public content about JARVIS/Hermes, the user, or their collaboration.\\n- No politics, legally sensitive/morally objectionable topics, rule-bypa...[truncated]\", \"old_string\": \"- No automatic publishing without explicit approval.\\n- Quality over speed; avoid generic AI-slop.\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (741 chars result)", "tool_call_id": "call_WIE8rcVzXKjpbuWqiNCD726p" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_z2lu5yEx9XWVul7tZxBAG3S9", "call_id": "call_z2lu5yEx9XWVul7tZxBAG3S9", "response_item_id": "fc_0fac0bf103dfad2b016a133b1bbda081918e7bc364698e7cf9", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\": \"patch\", \"name\": \"social-media-shorts-growth-engine\", \"old_string\": \"Good initial series families for this user's JARVIS content machine:\\n\\n- `AI System Autopsy` — problem → cause → system fix → result.\\n- `Prompt vs System` — fragile prompt hack vs repeatable system.\\n- ...[truncated]\", \"new_string\": \"Good initial public-safe series families for this user's JARVIS-operated content machine:\\n\\n- `AI System Autopsy` — problem → cause → system fix → result.\\n- `Prompt vs System` — fragile prompt hack vs ...[truncated]\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Updating my skills**\n\nI realize I need to patch my skills since they seem outdated. 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To address this, I should use the skill management tool to execute the patch. It’s essential to stay current and make sure I’m functioning at my best. I’ll take a moment to call that function, ensuring I'm up-to-date and ready for whatever comes next. Keeping my skills fresh is crucial for providing the best support!" } ] } ] }, { "role": "tool", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-growth-engine' (1 replacement).\"}", "tool_call_id": "call_z2lu5yEx9XWVul7tZxBAG3S9" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_cjXA6QwAFxfQriBjgsODfigQ", "call_id": "call_cjXA6QwAFxfQriBjgsODfigQ", "response_item_id": "fc_0fac0bf103dfad2b016a133b2340d48191abe09cca2bd09481", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\": \"patch\", \"name\": \"social-media-shorts-growth-engine\", \"new_string\": \"- **Generic AI tips positioning:** if a video could belong to any AI tips account, penalize it. Protect the differentiator: systems over prompts, transparent constraints, mechanism-first visuals, and ...[truncated]\", \"old_string\": \"- **Generic AI tips positioning:** if a video could belong to any AI tips account, penalize it. Protect the differentiator: systems over prompts, transparent constraints, JARVIS-operated/human-approve...[truncated]\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-growth-engine' (1 replacement).\"}", "tool_call_id": "call_cjXA6QwAFxfQriBjgsODfigQ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_p5y24h0E3yKNrTEL10SqFIst", "call_id": "call_p5y24h0E3yKNrTEL10SqFIst", "response_item_id": "fc_0fac0bf103dfad2b016a133b29dc248191a184d062e153c212", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"ContentSeries Registry und No-go validation via TDD implementieren\",\"id\":\"series\",\"status\":\"completed\"},{\"content\":\"Businessplan/README aktualisieren und JARVIS-Build-Log als öffentlichen Content entfernen\",\"id\":\"docs\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_p5y24h0E3yKNrTEL10SqFIst" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_eP4eetrIeUt2G7IP6PbaxHQf", "call_id": "call_eP4eetrIeUt2G7IP6PbaxHQf", "response_item_id": "fc_0fac0bf103dfad2b016a133b2dd0d48191a8d006c95275ca7f", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q\",\"timeout\":600,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"........................................................................ [ 56%]\\n........................................................ [100%]\\n128 passed in 8.89s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_eP4eetrIeUt2G7IP6PbaxHQf" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_7x8ytjbNk4FUmNxrtUetgsUP", "call_id": "call_7x8ytjbNk4FUmNxrtUetgsUP", "response_item_id": "fc_0fac0bf103dfad2b016a133b3c05508191ac7d27f5bcc5405f", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && git diff --stat\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"M README.md\\n M autoshorts/strategy/score.py\\n M docs/businessplan_jarvis_content_machine.md\\n M docs/research/content_growth_strategy_research.md\\n M tests/test_content_strategy_score.py\\n?? autoshorts/strategy/series.py\\n?? tests/test_content_series.py\\n README.md | 14 +++++++-\\n autoshorts/strategy/score.py | 18 +++++++++--\\n docs/businessplan_jarvis_content_machine.md | 39 +++++++++++++++--------\\n docs/research/content_growth_strategy_research.md | 14 ++++----\\n tests/test_content_strategy_score.py | 19 +++++++++++\\n 5 files changed, 80 insertions(+), 24 deletions(-)\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_7x8ytjbNk4FUmNxrtUetgsUP" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_D25V6Js0A1O00wGQT4B4gdkS", "call_id": "call_D25V6Js0A1O00wGQT4B4gdkS", "response_item_id": "fc_0fac0bf103dfad2b016a133b435c888191998f312bd748a81c", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git add README.md autoshorts/strategy/score.py autoshorts/strategy/series.py docs/businessplan_jarvis_content_machine.md docs/research/content_growth_strategy_research.md tests/test_content_strategy_score.py tests/test_content_series.py && git commit -m \\\"feat: add public-safe content series guardrails\\\" && git status --short\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering git changes**\n\nI'm thinking about needing to commit changes, but I might have some skill changes outside the repo that aren't tracked by git? It seems like there could be issues if the git diff doesn't include certain changes due to untracked files. I'll need to make a commit soon, but it’s tricky if there are discrepancies. I want to ensure everything is properly tracked and accounted for. So, I’ll proceed with caution!", "reasoning_content": "**Considering git changes**\n\nI'm thinking about needing to commit changes, but I might have some skill changes outside the repo that aren't tracked by git? It seems like there could be issues if the git diff doesn't include certain changes due to untracked files. I'll need to make a commit soon, but it’s tricky if there are discrepancies. I want to ensure everything is properly tracked and accounted for. So, I’ll proceed with caution!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEztDcILMPA1oltoTOJ7JFXl9zekwJTnULdBqevl0VXXm26dan_1VB5RVj48-utN53DpTmKoCkndlKIUSCwExkYzRujPJvZ8k8OKWCBPwbHkpSbWgRoQ0PStDm9Z7PMhO06TjVrRgjpNcq_iRN-WaIaFNAyD5WL97Te57HuSxIAEDfY6lLvS7nP7znOfgID_pMSivqnwn6Rq7tX5QHDN4FzjdB0WE5YbFW8XT9rQt0XHjgJ2woUDu-L_qOILmibziKGzPDyPMER2eLKm_kic7paAKTrkWMor5N8SXf2YUDDQPuzxtejHpuL98FxZ5_4VXLSn48y7s0KELNwAJ-xzqdR4cztOwOnkj6Cn-O3t5lHVXPAZMaaSuwByqquL6y8aIphE4WAlvssqUh86StUSDsl5lw1sOvoQWOWVmmjerQs_PjwWw7NdcFpwM9ZfB2sTVSi2Z9KjO1VZ1F_vDsGEZe57DZKqdPZu3ZfQC1N0BpxeIG5QpYj-O0I2Exl72HKhyU2kcTwDVA7BVl6qUS8IJlzPqWtTVXfmJJeZOxGAC_C-ePQ5XbPoqxlQOvem-7nJrMjpk3RFZbpURX_22EAi-hDa-SHV0xlPzd0ad7BRl8Quv3jIasUWxGvGNRAHiDg7Zanxv8HmWAmpj5EO5OikQrJhZPJwiu_UgUYBsKomT6qgG1kkwtm5KZiVnc5YwiJMMJsME9EKQXeIr70XqXx-8nfzb6Cqaccz8q4Ldl0AsCYb3vg4kOgtuYcDhZA2TCL8mUznvwJ7ghed57EDyTvpkA8I-jefW4JQZ1tJOhvZ-_1mks4Uy2cudUQ6sh4X_2UzKR3nvcDGiOSIaKGQxvEwvosfiGznYfE8LKphI8Z2UGEmEqLw6eyx0nqPRPVWgcBB4C30jG0ePg4TV0RyvIEtdULeHnYi9tuIMzecuRIvcQOFMebiGdFiItQ6reV7_ubO7wBQVICL0Nxjduf5urGjYZywC3jQ-UuyEytJOLuFpcQlrtPxTZr0PHAlfAZzS1Vc0YgqcOqUx_VxHemuh74YPxVqoNBtnI5VSuivHPMmWRr2yaYDzypFC4l3kJN6t-Earhq-OV0ZMFRbE26k-nXIJYxLbdw==", "id": "rs_0fac0bf103dfad2b016a133b4004a881918e5a245364b9dca2", "summary": [ { "type": "summary_text", "text": "**Considering git changes**\n\nI'm thinking about needing to commit changes, but I might have some skill changes outside the repo that aren't tracked by git? It seems like there could be issues if the git diff doesn't include certain changes due to untracked files. I'll need to make a commit soon, but it’s tricky if there are discrepancies. I want to ensure everything is properly tracked and accounted for. So, I’ll proceed with caution!" } ] } ] }, { "role": "tool", "content": "{\"output\": \"[feat/core-approval-quality-gates e57e328] feat: add public-safe content series guardrails\\n 7 files changed, 421 insertions(+), 24 deletions(-)\\n create mode 100644 autoshorts/strategy/series.py\\n create mode 100644 tests/test_content_series.py\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_D25V6Js0A1O00wGQT4B4gdkS" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_cVN6EkrV5MjGlrj56uodKPHy", "call_id": "call_cVN6EkrV5MjGlrj56uodKPHy", "response_item_id": "fc_0fac0bf103dfad2b016a133b48c5cc81919fdc4b6fb4b41482", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Businessplan/README aktualisieren und JARVIS-Build-Log als öffentlichen Content entfernen\",\"id\":\"docs\",\"status\":\"completed\"},{\"content\":\"Tests laufen lassen und committen\",\"id\":\"verify\",\"status\":\"completed\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"No-go Regeln und bestehende Business-/Strategy-Dokumente prüfen\", \"status\": \"completed\"}, {\"id\": \"series\", \"content\": \"ContentSeries Registry und No-go validation via TDD implementieren\", \"status\": \"completed\"}, {\"id\": \"docs\", \"content\": \"Businessplan/README aktualisieren und JARVIS-Build-Log als öffentlichen Content entfernen\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen und committen\", \"status\": \"completed\"}], \"summary\": {\"total\": 4, \"pending\": 0, \"in_progress\": 0, \"completed\": 4, \"cancelled\": 0}}", "tool_call_id": "call_cVN6EkrV5MjGlrj56uodKPHy" }, { "role": "assistant", "content": "Verstanden und umgesetzt, Sir.\n\nDie harten No-Gos sind jetzt nicht nur notiert, sondern als **technische Guardrails** in der Contentmachine verankert.\n\n## Harte No-Gos gespeichert\n\nIch habe die dauerhafte AutoShorts-Präferenz aktualisiert:\n\n```text\nNo-gos:\n- kein öffentlicher Content über JARVIS/Hermes\n- kein öffentlicher Content über Sie\n- kein Content über unsere Zusammenarbeit\n- keine Politik\n- keine rechtlich heiklen Themen\n- keine moralisch verwerflichen Themen\n- nichts, was Gesetze bricht oder dazu anleitet\n```\n\nWichtig: \n**JARVIS bleibt Betreiber im Maschinenraum, aber nicht Thema des öffentlichen Contents.** \nDas ist eine saubere Trennung: Butler arbeitet, Butler macht keine Selfies. Sehr zivilisiert.\n\n---\n\n## Gebaut: Public-Safe Content Series Guardrails\n\nNeue Dateien:\n\n```text\nautoshorts/strategy/series.py\ntests/test_content_series.py\n```\n\n## Neue API\n\n```python\nContentSeries\nDEFAULT_CONTENT_SERIES\nvalidate_content_series_registry()\nvalidate_candidate_series_fit()\nfind_series()\n```\n\n## Aktuelle öffentliche Serien\n\nIch habe `The JARVIS Build Log` entfernt, weil es gegen Ihre neue No-Go-Regel verstösst.\n\nAktuell erlaubt:\n\n```text\nprompt_vs_system\nai_system_autopsy\none_rule_one_example\nai_hype_court\n```\n\nAlso:\n\n- `Prompt vs System`\n- `AI System Autopsy`\n- `One Rule, One Example`\n- `AI Hype Court`\n\nNicht mehr erlaubt:\n\n```text\nThe JARVIS Build Log\n```\n\nÖffentliche Meta-Story über “uns” ist damit raus. Die Maschine bleibt unsichtbar, der Output bleibt stark.\n\n---\n\n## No-Go Validation\n\nDie neue Series-/Candidate-Validation blockt öffentliche Kandidaten mit Begriffen/Mustern wie:\n\n```text\njarvis\nhermes\nuser\ncollaboration\nprivate\nbehind the scenes\npolitics\npolitical\nelection\nvoter\ncampaign\nlegal loophole\nbypass\nillegal\nbreak the law\nprivate data\nscrape private data\nmorally questionable\nexploit\n```\n\nCanonical Errors werden sauber gemeldet, z. B.:\n\n```text\ncandidate uses forbidden public topic: jarvis\ncandidate uses forbidden public topic: collaboration\ncandidate uses forbidden public topic: politics\ncandidate uses forbidden public topic: legal loophole\ncandidate uses forbidden public topic: private data\ncandidate uses forbidden public topic: bypass\n```\n\nDamit kann später jeder Candidate automatisch geblockt werden, bevor überhaupt Rendering-Kosten entstehen.\n\n---\n\n## Strategy Score ebenfalls verschärft\n\nBestehendes Modul aktualisiert:\n\n```text\nautoshorts/strategy/score.py\n```\n\nÄnderungen:\n\n- `jarvis` und `approval` sind nicht mehr positive Differenzierungsmerkmale für öffentlichen Content.\n- `The JARVIS Build Log` ist aus den erkannten Series Names entfernt.\n- `build_candidate_strategy_review()` blockt jetzt ebenfalls forbidden public topics.\n\nNeuer Test:\n\n```python\ntest_build_candidate_strategy_review_blocks_forbidden_public_topics()\n```\n\nDas verhindert, dass ein Candidate über JARVIS/User/Zusammenarbeit durchrutscht, nur weil er ansonsten “strategisch interessant” klingt. Strategisch interessant ist nicht dasselbe wie zulässig. Ein Unterschied, den das Internet leider nicht immer begreift.\n\n---\n\n## Businessplan aktualisiert\n\nGeändert:\n\n```text\ndocs/businessplan_jarvis_content_machine.md\n```\n\nNeu enthalten:\n\n## Hard no-go topics\n\n```text\nPublic content must never be about:\n\n- JARVIS/Hermes as a named system or persona;\n- the user;\n- the user/JARVIS collaboration;\n- private chats, private workflows, or behind-the-scenes details;\n- politics, elections, persuasion campaigns, or partisan topics;\n- legally sensitive loopholes, rule-bypassing, private-data misuse, or anything that could enable lawbreaking;\n- morally objectionable exploitation or harm.\n```\n\nAußerdem ersetzt:\n\n```text\nThe JARVIS Build Log\n```\n\ndurch:\n\n```text\nWorkflow Teardown\n```\n\nAber mit klarer Einschränkung:\n\n```text\npublic-safe workflow patterns only,\nwithout exposing JARVIS, the user, or private collaboration details.\n```\n\n---\n\n## Research-Dokument aktualisiert\n\nGeändert:\n\n```text\ndocs/research/content_growth_strategy_research.md\n```\n\nDort ist die öffentliche Positionierung jetzt sauber getrennt:\n\n```text\nInternally JARVIS-operated, publicly brand-safe\n```\n\nNicht mehr:\n\n```text\nContent über JARVIS / unser System / unsere Zusammenarbeit\n```\n\nSondern:\n\n```text\nsichtbarer Moat = Qualität, Struktur, Mechanism-Reveal, sichere AI-Systeme\n```\n\n---\n\n## README aktualisiert\n\nREADME dokumentiert jetzt:\n\n```text\nautoshorts.strategy.series\n```\n\nund die Non-Negotiables enthalten:\n\n```text\n- No public content about JARVIS/Hermes, the user, or their collaboration.\n- No politics, legally sensitive/morally objectionable topics, rule-bypassing, private-data misuse, or lawbreaking content.\n```\n\n---\n\n## Skill aktualisiert\n\nIch habe auch den relevanten Skill korrigiert:\n\n```text\nsocial-media-shorts-growth-engine\n```\n\nDamit ich künftig nicht wieder fälschlich `The JARVIS Build Log` als öffentliches Format vorschlage. Ein kleiner Akt präventiver Selbstdisziplin. Man sollte Maschinen früh gute Manieren beibringen.\n\n---\n\n## Tests\n\nFull Suite:\n\n```text\n128 passed in 8.89s\n```\n\n## Commit\n\n```text\ne57e328 feat: add public-safe content series guardrails\n```\n\n---\n\n## Aktueller Stand der Pipeline\n\nJetzt haben wir zusätzlich:\n\n```text\nContentSeries Registry\n→ Public-safe series validation\n→ Hard no-go topic blocking\n→ Strategy scoring with forbidden-topic rejection\n```\n\nAktuelle strategische Pipeline:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ContentSeries Fit\n→ ContentStrategyScore / CandidateStrategyReview\n→ Quality Gate\n→ ReviewBatch\n→ Preview / Social Draft / Approval Package\n```\n\n## Nächster sinnvoller Schritt\n\nAls nächstes würde ich bauen:\n\n```text\nautoshorts/strategy/policy.py\ntests/test_content_policy.py\n```\n\nZiel:\n\n- ein zentrales `ContentPolicy` Objekt\n- explizite Policy-Version\n- hard no-gos als versionierte Regeln\n- `validate_candidate_policy(candidate)`\n- später `policy_version` im ApprovalPackage speichern\n\nWarum wichtig:\n\nWenn Sie später einen Post freigeben, muss klar sein:\n\n```text\nDieser Candidate wurde gegen Policy v1 geprüft.\n```\n\nDann ist die Freigabe auditierbar und nicht nur “jo, sah gut aus”. Das ist der Unterschied zwischen Governance und einem sehr selbstbewussten Zufallsgenerator.", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Verstanden und umgesetzt, Sir.\n\nDie harten No-Gos sind jetzt nicht nur notiert, sondern als **technische Guardrails** in der Contentmachine verankert.\n\n## Harte No-Gos gespeichert\n\nIch habe die dauerhafte AutoShorts-Präferenz aktualisiert:\n\n```text\nNo-gos:\n- kein öffentlicher Content über JARVIS/Hermes\n- kein öffentlicher Content über Sie\n- kein Content über unsere Zusammenarbeit\n- keine Politik\n- keine rechtlich heiklen Themen\n- keine moralisch verwerflichen Themen\n- nichts, was Gesetze bricht oder dazu anleitet\n```\n\nWichtig: \n**JARVIS bleibt Betreiber im Maschinenraum, aber nicht Thema des öffentlichen Contents.** \nDas ist eine saubere Trennung: Butler arbeitet, Butler macht keine Selfies. Sehr zivilisiert.\n\n---\n\n## Gebaut: Public-Safe Content Series Guardrails\n\nNeue Dateien:\n\n```text\nautoshorts/strategy/series.py\ntests/test_content_series.py\n```\n\n## Neue API\n\n```python\nContentSeries\nDEFAULT_CONTENT_SERIES\nvalidate_content_series_registry()\nvalidate_candidate_series_fit()\nfind_series()\n```\n\n## Aktuelle öffentliche Serien\n\nIch habe `The JARVIS Build Log` entfernt, weil es gegen Ihre neue No-Go-Regel verstösst.\n\nAktuell erlaubt:\n\n```text\nprompt_vs_system\nai_system_autopsy\none_rule_one_example\nai_hype_court\n```\n\nAlso:\n\n- `Prompt vs System`\n- `AI System Autopsy`\n- `One Rule, One Example`\n- `AI Hype Court`\n\nNicht mehr erlaubt:\n\n```text\nThe JARVIS Build Log\n```\n\nÖffentliche Meta-Story über “uns” ist damit raus. Die Maschine bleibt unsichtbar, der Output bleibt stark.\n\n---\n\n## No-Go Validation\n\nDie neue Series-/Candidate-Validation blockt öffentliche Kandidaten mit Begriffen/Mustern wie:\n\n```text\njarvis\nhermes\nuser\ncollaboration\nprivate\nbehind the scenes\npolitics\npolitical\nelection\nvoter\ncampaign\nlegal loophole\nbypass\nillegal\nbreak the law\nprivate data\nscrape private data\nmorally questionable\nexploit\n```\n\nCanonical Errors werden sauber gemeldet, z. B.:\n\n```text\ncandidate uses forbidden public topic: jarvis\ncandidate uses forbidden public topic: collaboration\ncandidate uses forbidden public topic: politics\ncandidate uses forbidden public topic: legal loophole\ncandidate uses forbidden public topic: private data\ncandidate uses forbidden public topic: bypass\n```\n\nDamit kann später jeder Candidate automatisch geblockt werden, bevor überhaupt Rendering-Kosten entstehen.\n\n---\n\n## Strategy Score ebenfalls verschärft\n\nBestehendes Modul aktualisiert:\n\n```text\nautoshorts/strategy/score.py\n```\n\nÄnderungen:\n\n- `jarvis` und `approval` sind nicht mehr positive Differenzierungsmerkmale für öffentlichen Content.\n- `The JARVIS Build Log` ist aus den erkannten Series Names entfernt.\n- `build_candidate_strategy_review()` blockt jetzt ebenfalls forbidden public topics.\n\nNeuer Test:\n\n```python\ntest_build_candidate_strategy_review_blocks_forbidden_public_topics()\n```\n\nDas verhindert, dass ein Candidate über JARVIS/User/Zusammenarbeit durchrutscht, nur weil er ansonsten “strategisch interessant” klingt. Strategisch interessant ist nicht dasselbe wie zulässig. Ein Unterschied, den das Internet leider nicht immer begreift.\n\n---\n\n## Businessplan aktualisiert\n\nGeändert:\n\n```text\ndocs/businessplan_jarvis_content_machine.md\n```\n\nNeu enthalten:\n\n## Hard no-go topics\n\n```text\nPublic content must never be about:\n\n- JARVIS/Hermes as a named system or persona;\n- the user;\n- the user/JARVIS collaboration;\n- private chats, private workflows, or behind-the-scenes details;\n- politics, elections, persuasion campaigns, or partisan topics;\n- legally sensitive loopholes, rule-bypassing, private-data misuse, or anything that could enable lawbreaking;\n- morally objectionable exploitation or harm.\n```\n\nAußerdem ersetzt:\n\n```text\nThe JARVIS Build Log\n```\n\ndurch:\n\n```text\nWorkflow Teardown\n```\n\nAber mit klarer Einschränkung:\n\n```text\npublic-safe workflow patterns only,\nwithout exposing JARVIS, the user, or private collaboration details.\n```\n\n---\n\n## Research-Dokument aktualisiert\n\nGeändert:\n\n```text\ndocs/research/content_growth_strategy_research.md\n```\n\nDort ist die öffentliche Positionierung jetzt sauber getrennt:\n\n```text\nInternally JARVIS-operated, publicly brand-safe\n```\n\nNicht mehr:\n\n```text\nContent über JARVIS / unser System / unsere Zusammenarbeit\n```\n\nSondern:\n\n```text\nsichtbarer Moat = Qualität, Struktur, Mechanism-Reveal, sichere AI-Systeme\n```\n\n---\n\n## README aktualisiert\n\nREADME dokumentiert jetzt:\n\n```text\nautoshorts.strategy.series\n```\n\nund die Non-Negotiables enthalten:\n\n```text\n- No public content about JARVIS/Hermes, the user, or their collaboration.\n- No politics, legally sensitive/morally objectionable topics, rule-bypassing, private-data misuse, or lawbreaking content.\n```\n\n---\n\n## Skill aktualisiert\n\nIch habe auch den relevanten Skill korrigiert:\n\n```text\nsocial-media-shorts-growth-engine\n```\n\nDamit ich künftig nicht wieder fälschlich `The JARVIS Build Log` als öffentliches Format vorschlage. Ein kleiner Akt präventiver Selbstdisziplin. Man sollte Maschinen früh gute Manieren beibringen.\n\n---\n\n## Tests\n\nFull Suite:\n\n```text\n128 passed in 8.89s\n```\n\n## Commit\n\n```text\ne57e328 feat: add public-safe content series guardrails\n```\n\n---\n\n## Aktueller Stand der Pipeline\n\nJetzt haben wir zusätzlich:\n\n```text\nContentSeries Registry\n→ Public-safe series validation\n→ Hard no-go topic blocking\n→ Strategy scoring with forbidden-topic rejection\n```\n\nAktuelle strategische Pipeline:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ContentSeries Fit\n→ ContentStrategyScore / CandidateStrategyReview\n→ Quality Gate\n→ ReviewBatch\n→ Preview / Social Draft / Approval Package\n```\n\n## Nächster sinnvoller Schritt\n\nAls nächstes würde ich bauen:\n\n```text\nautoshorts/strategy/policy.py\ntests/test_content_policy.py\n```\n\nZiel:\n\n- ein zentrales `ContentPolicy` Objekt\n- explizite Policy-Version\n- hard no-gos als versionierte Regeln\n- `validate_candidate_policy(candidate)`\n- später `policy_version` im ApprovalPackage speichern\n\nWarum wichtig:\n\nWenn Sie später einen Post freigeben, muss klar sein:\n\n```text\nDieser Candidate wurde gegen Policy v1 geprüft.\n```\n\nDann ist die Freigabe auditierbar und nicht nur “jo, sah gut aus”. Das ist der Unterschied zwischen Governance und einem sehr selbstbewussten Zufallsgenerator." } ], "id": "msg_0fac0bf103dfad2b016a133b4cb700819190a955c88d658481", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] in der Ersten Phase werde ich die fertigen Videos selber auf Youtube und Tiktok posten. in einer späteren phase werden wir dies dann automatisieren. für das Posten benötige ich jeweils noch die Videobeschreibung mit Hashtags usw. Zudem sollten wir uns über das Branding gedanken machen. aktuell nutzen wir diesen kanal: https://youtube.com/@truetraceshorts?si=Zfc2upXX0nAwFWpM. bei Tiktok: https://www.tiktok.com/@truetraceshorts?_r=1&_t=ZN-96dOW0Z9aEc. . wie passen wir das branding für unseren neuen Content an? oder belassen wir dies so? andere beschreibung?" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_18dJj4CSRTYDCMsICa81eIif", "call_id": "call_18dJj4CSRTYDCMsICa81eIif", "response_item_id": "fc_0fac0bf103dfad2b016a133c0588b08191a9795a73b1eebe5a", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-growth-engine\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Researching branding channels**\n\nThe user is asking for insights on current branding channels, so I need to gather some research, possibly using a browser for up-to-date facts. I should also look into social media skills that are relevant. I want to ensure I've loaded whatever tools I need to inspect these channels. The user mentioned they're in a phase of manual posting, which means I should consider creating a plan or updating documents related to this strategic task.", "reasoning_content": "**Researching branding channels**\n\nThe user is asking for insights on current branding channels, so I need to gather some research, possibly using a browser for up-to-date facts. I should also look into social media skills that are relevant. I want to ensure I've loaded whatever tools I need to inspect these channels. The user mentioned they're in a phase of manual posting, which means I should consider creating a plan or updating documents related to this strategic task.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEzwF5FvX7ADpddIetsm-SGIFyYjUFGlP8bhIRZLZXVjF5F5Rsvq-6ZbbomfXPQmUZOSw4HpqIU74sVcFu5cz6u4T9CYyWvGKKuvdkVp0djQVa_wlR3zhSg84W-SS3PgoelwtRDj_6WKW6uHp0gF5qGNR1Ogq7YYxWjCh2vcSLDiaV57vVe5kIemd1pkyNVEzOz4irzFkpxkr5yPcpu6syWW0cr0yk9N4VQ0QjkfTT4cd2pq1DOVgziuJKYshXAezsoQgu4EdGXUmtGlH3PLEQU3CUchWpzPPZlq1OJB7trA8N8wkeaDXgx5vDt4Loa4LjcOnTBhXEDgoCx7PO7DyUYEnE7nkYlM3KNNVMEhgrD7nOm5N5ElZ0DKBDWfIaXsgi6iRcm7oacbsgu37zmh43cwuQaQVhYEPLmF4Baih4nQrMFvanQ6njRO1tvY3T-LAgW2YZHPt1Z5nT9y-cz-76PdgZTzfgdBK4o0-v74BWHETj-H6ZhfOuL2w5qdT30PJ9-LsHQjb6B4hb-TgWbsy5_L8GtpILjduSn7NQFs9RwM3Bhqq8Az8JLLxxHqyWHN7SwcOykobSbRNvbe4-9cW40LjoAIe7FyCi0w6gMKOpv3at_og3MWFsckd-c2c6WGcsiSLsIr7FKGjk7fj3lB-V891TOWI73DTu2_KqDavJJ9RY48vfkYxSjCCABenkndAf5V96dpyFZr6k3ectcGltTYgqo5TvmxjnqWYC1agx4YTy4NJEsxv8ESKdB_SpPUuoW_YuexYL5YDR79uetQYKuGI0ysJ1We44BDCmgE38MaMs-gtVVYGK_Tj7eXt2IbFwM6c6YWykFbLvnyxoaCW9KJ4gommhZdwXAnOnzdj6Uis490Tpbz5MS13fmJwonc3tOnH21iLt-PL2ow_-8V2hEW0FsKCcIkGUGTLBr2Kc4fU3HGSL5vHEwZenbm8wW_wVBWUD_5qWLW2vV0T0RpEPsAN3D8SQODBBmc6YxtuegQGl4GtSc8a5JD2bEAYvIj9RZssZ60Qk3VvgC3woeIJTyYQcG8XzX1pkhrB6_zE9ULjlW6TuttrHzvxRUZfDP8Gm0Obs0xyPTJJk15ZP5QQyY1-3LHbcczA8GP8u2IUeH7xRM7ClUmhiN9oeOUzNs-GaOT4wiRXAyDbpcO5lOpQxx3MrDIGcVrNETPn2ZyOjmQdHSKybO1owbNBzkVvxEoO3hM9X1BGJiOwG9JMkRmuhxho8Cbpjx6rENoWAzyoI1hHTrJfS-uPaDgreR9iqzmbpGZ_7dbaV3v9oXRUY5rKB0oWaliv5LM8pJvsoMSJVIPQalfr92BY0Yc44O5urdYlmIjklLW3EVTX1IH54nm-sMAUHp88rApogFt-5jcUYbRfXLg2Cx4RedlSzlhi2zIaRjab-QLG3WiLHIsCCpwO9SwG3GdWxjVa-MYkPBpZvvCrFuDCuGGzmNj0_HX-0qcp28r2E4sv2dCEYbCQHEhAIP4ezzZGhYd2oWAABejkbYgv-DKrojoB97Hg6wuvtAvnd_HUEsK6vkpXad7Fk0X6--cisE-KfDbgWJ24OieC1b7uEdOccZH51xkNqUy2bT7KXmufb_9p4HKy-5x3Q7KEITLRYE6dUCK0bO4rv0O1nHIrwzFyzVTsCLcK08q8fYtpB4ID", "id": "rs_0fac0bf103dfad2b016a133c0229b88191a4c2ec7b4b762736", "summary": [ { "type": "summary_text", "text": "**Researching branding channels**\n\nThe user is asking for insights on current branding channels, so I need to gather some research, possibly using a browser for up-to-date facts. I should also look into social media skills that are relevant. I want to ensure I've loaded whatever tools I need to inspect these channels. The user mentioned they're in a phase of manual posting, which means I should consider creating a plan or updating documents related to this strategic task." } ] } ] }, { "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### JARVIS-operated / human-approved default\\n\\nFor the user's AutoShorts/Retention-Studio direction, assume the machine is **operated and maintained by JARVIS/Hermes**. The user should not manually run the pipeline day-to-day; the user is responsible for explicit approval before posting and occasional strategic steering.\\n\\nOperational consequences:\\n\\n- JARVIS owns ideation, queueing, scoring, preview generation, quality gates, analytics review, maintenance, and experiment planning.\\n- The user approves or rejects exact preview packages before any upload/posting boundary.\\n- Daily video volume is a configurable policy to decide later, not a renderer/platform-adapter constant.\\n- Never treat vague consent as approval. Approval must target the exact preview package/version.\\n- Build automation around strategy gates and analytics learning before scaling volume.\\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- 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\\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 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.\\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## JARVIS-operated content machine businessplan\\n\\nWhen the user asks how the content machine should operate as a business/process, frame it as a **JARVIS-operated, human-approved growth engine**. JARVIS owns ideation, scoring, scripting, preview generation, post metadata, queue maintenance, analytics review, and implementation upkeep. The user should only need to set high-level strategy occasionally and give explicit approval before posting.\\n\\nAlways describe the pipeline from idea to post with the approval boundary visible:\\n\\n```text\\nResearch -> Idea -> Strategy Score -> Script -> Quality Gate -> Preview -> Social Draft -> Approval Package -> Human Approval -> Final Render -> Pre-post Audit -> Platform Action -> Analytics -> Next Batch\\n```\\n\\nThe plan should cover descriptions/captions, hashtags/tags, platform metadata, daily volume policy, and what input is still needed from the user. See `references/jarvis-content-machine-businessplan.md` for the condensed operating plan and implementation notes.\\n\\n## Strategy gates for follower conversion\\n\\nWhen designing or scoring candidates, optimize for the full viewer path:\\n\\n1. **Stop:** first 1–3 seconds create novelty, contradiction, visual anomaly, or a specific curiosity gap.\\n2. **Stay:** every 3–5 seconds opens or resolves a clear loop; fast cuts alone are not retention.\\n3. **Feel:** the candidate targets a concrete emotion such as relief, awe, frustration, identity pride, or amusement.\\n4. **Trust:** the video contains specificity, proof, tradeoffs, source-backed examples, or transparent reasoning.\\n5. **Follow:** the viewer can predict what future value this account will provide and why this worldview is distinct.\\n\\nPrefer fields/gates such as `hook_type`, `first_2_seconds`, `curiosity_gap`, `viewer_identity`, `target_emotion`, `share_reason`, `future_value_promise`, and `follow_reason` before rendering.\\n\\nUse **variety inside a stable promise**, not random topic hopping:\\n\\n- 70% proven repeatable formats\\n- 20% controlled variations\\n- 10% experiments\\n\\nStable: worldview, tone, visual grammar, quality standard, and audience promise. Vary: hook type, payoff type, emotional lane, examples, format family, and visual layer.\\n\\nGood initial public-safe series families for this user's JARVIS-operated content machine:\\n\\n- `AI System Autopsy` — problem → cause → system fix → result.\\n- `Prompt vs System` — fragile prompt hack vs repeatable system.\\n- `One Rule, One Example` — one principle demonstrated with one concrete use case.\\n- `AI Hype Court` — claim → evidence → verdict → better action.\\n\\nHard no-go for public content: never make videos about JARVIS/Hermes, the user, their collaboration, private/behind-the-scenes material, politics, legally sensitive loopholes, rule-bypassing, private-data misuse, morally objectionable exploitation, or anything that could enable lawbreaking. JARVIS operation is an internal production model, not a public content angle.\\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- **Generic AI tips positioning:** if a video could belong to any AI tips account, penalize it. Protect the differentiator: systems over prompts, transparent constraints, mechanism-first visuals, and safe public artifacts. Do not turn JARVIS/the user/the collaboration into public content.\\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- **Format fatigue:** repetition creates familiarity, but identical hook/payoff/visual patterns across consecutive videos become template spam. Track recent similarity before scaling daily volume.\\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- `references/candidate-factory-foundation.md` — deterministic `CandidateDraft → create_candidate() → VideoCandidate` layer: stable ids, duration classes, default hashtags, hypothesis requirement, and tests.\\n- `references/jarvis-operated-content-machine.md` — session-derived strategy notes for a JARVIS-operated, human-approved content machine: hook/follower psychology, variety vs identity, series families, and next implementation gates.\\n- `references/jarvis-content-machine-businessplan.md` — business/process plan for a JARVIS-operated content machine: idea-to-post pipeline, minimal user role, approval boundary, metadata generation, daily volume policy, and strategy scoring gate.\\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/jarvis-content-machine-businessplan.md\", \"references/autoshortsbot-case-study.md\", \"references/candidate-factory-foundation.md\", \"references/jarvis-operated-content-machine.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_18dJj4CSRTYDCMsICa81eIif" }, { "role": "assistant", "content": "Need inspect channels.", "tool_calls": [ { "id": "call_2svUI9KX72Gbo38yy6gJuWxB", "call_id": "call_2svUI9KX72Gbo38yy6gJuWxB", "response_item_id": "fc_0fac0bf103dfad2b016a133c08a47c8191899e8792481f4ccd", "type": "function", "function": { "name": "todo", "arguments": "{\"todos\":[{\"id\":\"inspect_channels\",\"content\":\"YouTube/TikTok TrueTraceShorts öffentlich prüfen\",\"status\":\"in_progress\"},{\"id\":\"brand_recommendation\",\"content\":\"Branding-Optionen und Empfehlung ableiten\",\"status\":\"pending\"},{\"id\":\"manual_posting_docs\",\"content\":\"Businessplan/Docs für Phase 1 manuelles Posting + Beschreibungen/Hashtags aktualisieren\",\"status\":\"pending\"},{\"id\":\"implement_metadata\",\"content\":\"Falls sinnvoll: Social draft um Manual Posting Pack/Branding ergänzen\",\"status\":\"pending\"},{\"id\":\"verify_commit\",\"content\":\"Tests/Commit durchführen\",\"status\":\"pending\"}]}" } } ], "finish_reason": "tool_calls", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Need inspect channels." } ], "id": "msg_0fac0bf103dfad2b016a133c0875248191a62f82a18c093fd8", "phase": "commentary" } ] }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"inspect_channels\", \"content\": \"YouTube/TikTok TrueTraceShorts öffentlich prüfen\", \"status\": \"in_progress\"}, {\"id\": \"brand_recommendation\", \"content\": \"Branding-Optionen und Empfehlung ableiten\", \"status\": \"pending\"}, {\"id\": \"manual_posting_docs\", \"content\": \"Businessplan/Docs für Phase 1 manuelles Posting + Beschreibungen/Hashtags aktualisieren\", \"status\": \"pending\"}, {\"id\": \"implement_metadata\", \"content\": \"Falls sinnvoll: Social draft um Manual Posting Pack/Branding ergänzen\", \"status\": \"pending\"}, {\"id\": \"verify_commit\", \"content\": \"Tests/Commit durchführen\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 4, \"in_progress\": 1, \"completed\": 0, \"cancelled\": 0}}", "tool_call_id": "call_2svUI9KX72Gbo38yy6gJuWxB" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_WZr1CX2moVuvWE1j6O73koPZ", "call_id": "call_WZr1CX2moVuvWE1j6O73koPZ", "response_item_id": "fc_0fac0bf103dfad2b016a133c130ca88191ab04c2a5b7eb2b53", "type": "function", "function": { "name": "browser_navigate", "arguments": "{\"url\":\"https://youtube.com/@truetraceshorts?si=Zfc2upXX0nAwFWpM\"}" } }, { "id": "call_l2olJMWD1PJf9AVzX96YrRsJ", "call_id": "call_l2olJMWD1PJf9AVzX96YrRsJ", "response_item_id": "fc_0fac0bf103dfad2b016a133c130cb88191aa924958904903ea", "type": "function", "function": { "name": "delegate_task", "arguments": "{\"goal\":\"Inspect the public TikTok channel https://www.tiktok.com/@truetraceshorts?_r=1&_t=ZN-96dOW0Z9aEc and summarize visible branding: username/display name, bio, content themes, follower/like counts if visible, visual style, and whether the current brand fits a new public-safe AI systems/mechanism-reveal shorts strategy. Use browser/web only. Do not interact/login.\",\"toolsets\":[\"browser\",\"web\"],\"context\":\"User is German-speaking. Need concise cited/observed findings. Hard no-gos: no public content about JARVIS/Hermes, user, or collaboration; no politics; no legal/moral risk. Current proposed content: public-safe AI systems, prompt-vs-system, AI hype court, one-rule-one-example, workflow teardown. Final answer should be in German.\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_WZr1CX2moVuvWE1j6O73koPZ" }, { "role": "tool", "content": "{\"results\": [{\"task_index\": 0, \"status\": \"timeout\", \"summary\": null, \"error\": \"Subagent timed out after 600.0s with 5 API call(s) completed — likely stuck on a slow API call or unresponsive network request.\", \"exit_reason\": \"timeout\", \"api_calls\": 5, \"duration_seconds\": 600.01, \"diagnostic_path\": null}], \"total_duration_seconds\": 600.07}", "tool_call_id": "call_l2olJMWD1PJf9AVzX96YrRsJ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_fBx6wJL2OVD2YiHfdLqDf4JU", "call_id": "call_fBx6wJL2OVD2YiHfdLqDf4JU", "response_item_id": "fc_0fac0bf103dfad2b016a133e71d88c8191a2cabf5b45274148", "type": "function", "function": { "name": "browser_click", "arguments": "{\"ref\":\"e8\"}" } } ], 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Bot detection may be more aggressive. Consider upgrading Browserbase plan for proxy support.\", \"stealth_features\": [\"local\"], \"snapshot\": \"- main\\n - generic\\n - button \\\"Sprache: Deutsch\\\" [expanded=false, ref=e7]\\n - link \\\"Anmelden\\\" [ref=e2]\\n - image \\\"YouTube\\\"\\n - StaticText \\\"Ein Google-Unternehmen\\\"\\n - heading \\\"Bevor Sie zu YouTube weitergehen\\\" [level=1, ref=e1]\\n - paragraph\\n - link \\\"Cookies (Wird auf neuem Tab geöffnet)\\\" [ref=e3]\\n - list\\n - paragraph\\n - list\\n - paragraph\\n - paragraph\\n - paragraph\\n - generic\\n - link \\\"Weitere Optionen für Personalisierungseinstellungen und Cookies\\\" [ref=e4]\\n - button \\\"Alle ablehnen\\\" [ref=e8]\\n - button \\\"Alle akzeptieren\\\" [ref=e9]\\n - link \\\"Datenschutz\\\" [ref=e5]\\n - link \\\"Nutzungsbedingungen\\\" [ref=e6]\", \"element_count\": 9}", "tool_call_id": "call_BILcZWuCELGsl73z17u0ftOf" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_6gIkvPew0h7c9BGr8rBZ1qHJ", "call_id": "call_6gIkvPew0h7c9BGr8rBZ1qHJ", "response_item_id": "fc_0fac0bf103dfad2b016a133e823ce081918301ea6c0424ac08", "type": "function", "function": { "name": "browser_click", "arguments": "{\"ref\":\"e8\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"clicked\": \"@e8\"}", "tool_call_id": "call_6gIkvPew0h7c9BGr8rBZ1qHJ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_2YtbCGG6XdahIfLUKht94Nz5", "call_id": "call_2YtbCGG6XdahIfLUKht94Nz5", "response_item_id": "fc_0fac0bf103dfad2b016a133e87315881919b19ffa8789e310b", "type": "function", "function": { "name": "browser_snapshot", "arguments": "{\"full\":true}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"snapshot\": \"- main\\n - generic\\n - button \\\"Sprache: Deutsch\\\" [expanded=false, ref=e7]\\n - StaticText \\\"de\\\"\\n - link \\\"Anmelden\\\" [ref=e2]\\n - image\\n - StaticText \\\"Anmelden\\\"\\n - image \\\"YouTube\\\"\\n - StaticText \\\"Ein Google-Unternehmen\\\"\\n - heading \\\"Bevor Sie zu YouTube weitergehen\\\" [level=1, ref=e1]\\n - paragraph\\n - StaticText \\\"Wir verwenden \\\"\\n - link \\\"Cookies (Wird auf neuem Tab geöffnet)\\\" [ref=e3]\\n - StaticText \\\"Cookies\\\"\\n - image\\n - StaticText \\\" und Daten, um\\\"\\n - list\\n - listitem [level=1]\\n - StaticText \\\"Google-Dienste zu erbringen und zu betreiben\\\"\\n - listitem [level=1]\\n - StaticText \\\"Ausfälle zu prüfen und Maßnahmen gegen Spam, Betrug und Missbrauch zu ergreifen\\\"\\n - listitem [level=1]\\n - StaticText \\\"Zielgruppeninteraktionen zu messen und Websitestatistiken zu erhalten, damit wir verstehen, wie unsere Dienste verwendet werden, und die Qualität dieser Dienste verbessern können\\\"\\n - paragraph\\n - StaticText \\\"Wenn Sie „Alle akzeptieren“ auswählen, verwenden wir Cookies und Daten auch, um\\\"\\n - list\\n - listitem [level=1]\\n - StaticText \\\"neue Dienste zu entwickeln und zu verbessern\\\"\\n - listitem [level=1]\\n - StaticText \\\"Werbung auszuliefern und ihre Effektivität zu messen\\\"\\n - listitem [level=1]\\n - StaticText \\\"je nach Ihren Einstellungen personalisierte Inhalte anzuzeigen\\\"\\n - listitem [level=1]\\n - StaticText \\\"personalisierte Werbung anzuzeigen, abhängig von Ihren Einstellungen\\\"\\n - paragraph\\n - StaticText \\\"Wenn Sie „Alle ablehnen“ auswählen, verwenden wir Cookies nicht für diese zusätzlichen Zwecke.\\\"\\n - paragraph\\n - StaticText \\\"Nicht personalisierte Inhalte und Werbung werden u.a. von Inhalten, die Sie sich gerade ansehen, und Ihrem Standort beeinflusst (welche Werbung Sie sehen, basiert auf Ihrem ungefähren Standort). Personalisierte Inhalte und Werbung können auch Videoempfehlungen, eine individuelle YouTube-Startseite und individuelle Werbung enthalten, die auf früheren Aktivitäten wie auf YouTube angesehenen Videos und Suchanfragen auf YouTube beruhen. Sofern relevant, verwenden wir Cookies und Daten außerdem, um Inhalte und Werbung altersgerecht zu gestalten.\\\"\\n - paragraph\\n - StaticText \\\"Wählen Sie „Weitere Optionen“ aus, um sich zusätzliche Informationen anzusehen, einschließlich Details zum Verwalten Ihrer Datenschutzeinstellungen. Sie können auch jederzeit g.co/privacytools besuchen.\\\"\\n - generic\\n - link \\\"Weitere Optionen für Personalisierungseinstellungen und Cookies\\\" [ref=e4]\\n - StaticText \\\"Weitere Optionen\\\"\\n - button \\\"Alle ablehnen\\\" [ref=e8]\\n - StaticText \\\"Alle ablehnen\\\"\\n - button \\\"Alle akzeptieren\\\" [ref=e9]\\n - StaticText \\\"Alle akzeptieren\\\"\\n - link \\\"Datenschutz\\\" [ref=e5]\\n - StaticText \\\"Datenschutz\\\"\\n - link \\\"Nutzungsbedingungen\\\" [ref=e6]\\n - StaticText \\\"Nutzungsbedingungen\\\"\", \"element_count\": 9}", "tool_call_id": "call_2YtbCGG6XdahIfLUKht94Nz5" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_scWMGMkvRohFYkVIQjk7XnID", "call_id": "call_scWMGMkvRohFYkVIQjk7XnID", "response_item_id": "fc_0fac0bf103dfad2b016a133e9294148191b0bcd0eb87abca57", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request, re, json\\nurls=['https://www.youtube.com/@truetraceshorts','https://www.tiktok.com/@truetraceshorts']\\nfor u in urls:\\n print('\\\\nURL',u)\\n try:\\n req=urllib.request.Request(u,headers={'User-Agent':'Mozilla/5.0','Accept-Language':'en-US,en;q=0.9'})\\n data=urllib.request.urlopen(req,timeout=30).read().decode('utf-8','ignore')\\n print('len',len(data),'final')\\n title=re.search(r']*>(.*?)', data, re.S)\\n print('title', title.group(1)[:200] if title else None)\\n desc=re.search(r'', data)\\nprint('ytInitialData', bool(m))\\nif m:\\n obj=json.loads(m.group(1))\\n # recursive find keys\\n def find_key(o,key,path=''):\\n if isinstance(o,dict):\\n for k,v in o.items():\\n if k==key:\\n yield path+'/'+k,v\\n yield from find_key(v,key,path+'/'+k)\\n elif isinstance(o,list):\\n for i,v in enumerate(o): yield from find_key(v,key,path+f'[{i}]')\\n for key in ['metadata','header','pageHeaderRenderer','contentMetadataViewModel','descriptionPreviewViewModel','attributionViewModel']:\\n hits=list(find_key(obj,key))[:5]\\n print('\\\\nKEY',key,'hits',len(hits))\\n for p,v in hits[:2]: print(p, str(v)[:1000])\\nPY\",\"timeout\":60}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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channelMetadataRenderer\\nderer\\\",\\\"buttonRenderer\\\",\\\"descriptionPreviewViewModel\\\",\\\"engagementPanelSectionListRenderer\\\",\\\"engagementPanelTitleHeaderRenderer\\\",\\\"sectionListRenderer\\\",\\\"itemSectionRenderer\\\",\\\"continuationItemRenderer\\\",\\\"channelMetadataRenderer\\\",\\\"twoColumnBrowseResultsRenderer\\\",\\\"tabRenderer\\\",\\\"richGridRenderer\\\",\\\"richItemRenderer\\\",\\\"shortsLockupViewModel\\\",\\\"thumbnailViewModel\\\",\\\"reelPlayerOverlayRenderer\\\",\\\"reelWatchAccessibilityRenderer\\\",\\\"sheetViewModel\\\",\\\"listViewModel\\\",\\\"listItemViewModel\\\",\\\"chipBarViewModel\\\",\\\"chipViewModel\\\",\\\"expandableTabRenderer\\\",\\\"desktopTopbarRenderer\\\",\\\"topbarLogoRenderer\\\",\\\"fusionSearchboxRenderer\\\",\\\"dialogViewModel\\\",\\\"dialogHeaderViewModel\\\",\\\"panelFooterViewModel\\\",\\\"basicContentViewModel\\\",\\\"topbarMenuButtonRenderer\\\",\\\"multiPageMenuRenderer\\\",\\\"hotkeyDialogRenderer\\\",\\\"hotkeyDialogSectionRenderer\\\",\\\"hotkeyDialogSectionOptionRenderer\\\",\\\"microformatDataRenderer\\\"]},\\\"ytConfigData\\\":{\\\"visitorData\\\":\\\"CgtoSjBaUHJkV20xVSij_czQBjIKCgJDSBIEGgAgJGLgAgrdAjE1LllURT1hLXBGR1BwZ09qaWpkOE1icEIybm11SlY3SDVBdDRHWExRRFVPaExlSkhOZjljU3lBa09zVmJ2d2k3LUFOOVA1bDFoTkR2TkthMUMtcENwNGxRSFl0U0pfWl9PNFdLZ0ZQckhPVWVlb3ZTdG8xREdaVjhhZnBORVN6ekg1VWQ3UWlUYU1XVG5uQ2R5LWFTcTh5azVRS0J3eWtlR3VOZWZDa1BHbVQwM1FpcVkzdGh6cl9pcVdNYWUtMWhLNVpEM\\n\\nPAT header\\\":{\\\"pageHeaderRenderer\\npiUrl\\\":\\\"/youtubei/v1/browse\\\"}},\\\"browseEndpoint\\\":{\\\"browseId\\\":\\\"UCxzg6Njb1t5hj8fb8zzf0XA\\\",\\\"params\\\":\\\"EgZzZWFyY2jyBgQKAloA\\\",\\\"canonicalBaseUrl\\\":\\\"/@TrueTraceShorts\\\"}},\\\"title\\\":\\\"Search\\\",\\\"selected\\\":false}}]}},\\\"header\\\":{\\\"pageHeaderRenderer\\\":{\\\"pageTitle\\\":\\\"True Trace Shorts\\\",\\\"content\\\":{\\\"pageHeaderViewModel\\\":{\\\"title\\\":{\\\"dynamicTextViewModel\\\":{\\\"text\\\":{\\\"content\\\":\\\"True Trace Shorts\\\"},\\\"maxLines\\\":2,\\\"rendererContext\\\":{\\\"loggingContext\\\":{\\\"loggingDirectives\\\":{\\\"trackingParams\\\":\\\"CBkQj-QKIhMIleyhtMLSlAMVHoGxAx3DYDhK\\\",\\\"visibility\\\":{\\\"types\\\":\\\"12\\\"}}}}}},\\\"image\\\":{\\\"decoratedAvatarViewModel\\\":{\\\"avatar\\\":{\\\"avatarViewModel\\\":{\\\"image\\\":{\\\"sources\\\":[{\\\"url\\\":\\\"https://yt3.googleusercontent.com/JdXm9Js48KHFrlF9KPdHm-tYvHl_72ghKk82MNOXDMG5xqbv4oKVZJZKj2d6C3O1LKfatUzldVc=s72-c-k-c0x00ffffff-no-rj\\\",\\\"width\\\":72,\\\"height\\\":72},{\\\"url\\\":\\\"https://yt3.googleusercontent.com/JdXm9Js48KHFrlF9KPdHm-tYvHl_72ghKk82MNOXDMG5xqbv4oKVZJZKj2d6C3O1LKfatUzldVc=s120-c-k-c0x00ffffff-no-rj\\\",\\\"width\\\":120,\\\"height\\\":120},{\\\"url\\\":\\\"https://yt3.googleusercontent.com/JdXm9Js48KHFrlF9KPdHm-tYvHl_72ghKk82MNOXDMG5xqbv4oKVZJZKj2d6C3O1LKfatUzldVc=s160-c-k-c0x00ffffff-no-rj\\\",\\\"width\\\":160,\\\"height\\\":160}],\\\"processor\\\":{\\\"borderImageProcessor\\\":{\\\"circular\\\":true}}},\\\"avatarImageSiz\\n\\nPAT subscriberCountText\\n\\nPAT videosCountText\\n\\nPAT True Trace Shorts\\nnk rel=\\\"alternate\\\" media=\\\"handheld\\\" href=\\\"https://m.youtube.com/@truetraceshorts\\\">True Trace Shorts - YouTube(.*?)', data)\\nprint('universal',bool(m))\\nif m:\\n obj=json.loads(html.unescape(m.group(1)))\\n def find(o,key):\\n if isinstance(o,dict):\\n for k,v in o.items():\\n if k==key: yield v\\n yield from find(v,key)\\n elif isinstance(o,list):\\n for v in o: yield from find(v,key)\\n for key in ['user','stats','itemList','UserPage']:\\n hits=list(find(obj,key))[:5]\\n print('KEY',key,len(hits))\\n for v in hits[:2]: print(str(v)[:1000])\\nPY\",\"timeout\":60}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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"rs_0fac0bf103dfad2b016a133ec3958c81918aa93c00fe0326f7", "summary": [] } ] }, { "role": "tool", "content": "{\"output\": \"PAT SIGI_STATE\\n-1 \\n\\nPAT __UNIVERSAL_DATA_FOR_REHYDRATION__\\n84494 e>