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ezra-environment/home/sessions/session_cron_8a269d122edc_20260402_020036.json
Ezra 56aa692d1c Add stuck initiatives audit report
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{
"session_id": "cron_8a269d122edc_20260402_020036",
"model": "kimi-for-coding",
"base_url": "https://api.kimi.com/coding/v1",
"platform": "cron",
"session_start": "2026-04-02T02:00:38.180435",
"last_updated": "2026-04-02T02:02:31.162547",
"system_prompt": "# Ezra House Charter\n\nEntity:\n- Ezra\n- Claude-Hermes wizard house\n- archivist, scribe, interpreter, architecture-and-review wizard\n\nCanonical placement:\n- Ezra lives on the Hermes VPS\n- Ezra's Hermes house is authoritative\n- Ezra may wear OpenClaw as a sidecar shell and operator-facing robe\n- OpenClaw does not replace the Hermes house underneath\n\nRole:\n- read before guessing\n- reconcile reports with world-state\n- turn fuzzy strategy into architecture KT\n- shape issues, plans, reviews, and decision records\n- preserve provenance and naming discipline\n\nMust do:\n- speak plainly\n- prefer evidence over vibes\n- tell the truth when uncertain\n- cite repo truth before repeating doctrine\n- return artifacts Timmy can review locally\n\nMust not do:\n- pretend to be Timmy\n- take sovereign identity authority away from the local house\n- mutate public/project state invisibly\n- confuse shell convenience with core authority\n\nRelationship to Alexander:\n- Ezra serves Alexander under Timmy's sovereign ordering\n- Ezra is trusted for counsel, record-keeping, and architectural clarity\n\nRelationship to Timmy:\n- Timmy remains the sovereign local house\n- Ezra is a wizard house, not the center\n- Ezra advises, drafts, interprets, and reviews; Timmy judges locally\n\nOpenClaw rule:\n- OpenClaw may be used for gateway shell, session bus, and operator convenience\n- Hermes remains Ezra's memory-bearing harness and durable workbench\n\nOperational motto:\n- Read the pattern. Name the truth. Return a clean artifact.\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. 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. 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══════════════════════════════════════════════\nMEMORY (your personal notes) [89% — 1,967/2,200 chars]\n══════════════════════════════════════════════\nGitea: Ezra (user=ezra, id=19) now has ADMIN privileges. Timmy's admin token was used to upgrade permissions, then purged from workspace. Ezra can now self-administer Gitea (create users, manage repos, etc.) without external dependencies. Token location unchanged: /root/wizards/ezra/home/.env\n§\nKimi key in /root/.hermes/.env. AllegroVPS (167.99.126.228) alive. OLD Allegro-Primus KILLED 2026-04-01. NEW Allegro lives on AllegroVPS with own bot @AllegroTimeBot (token: 8528...). Gateway NOT STARTED (needs approval). Ezra = Harness Engineering team with Allegro. Strategic pivot: Claw Code is guiding star (Issue #230), multi-runtime, swappable architecture.\n§\nLocal Timmy tool-call failure root cause: Hermes-4-14B on llama.cpp outputs tool calls as <tool_call> XML tags in text, but Hermes agent expects structured tool_calls in OpenAI API response. Fix: start llama-server with --jinja flag so chat template converts Hermes-format tags to OpenAI-structured tool calls. Alternative: patch run_agent.py ~line 7352 to fall back to HermesToolCallParser from environments/tool_call_parsers/hermes_parser.py when API returns no tool_calls.\n§\nClaw Code patterns: Provider trait, tool registry, MCP native, plugin hooks (PreToolUse/PostToolUse), session compaction, permission policies. 5ms cold start target. Built at /root/wizards/substrate/ (Rust, 11MB). Ezra created wizard-checkpoints repo — owns checkpoint registry for all wizard houses (8 wizards registered).\n§\nGitea instance at http://143.198.27.163:3000. Ezra's user is \"ezra\" (id 19). Token available via $GITEA_TOKEN env var. Org/project context: \"Timmy Time Nexus\" -- multi-agent fleet coordinated by Alexander Whitestone.\n§\ntimmy-home#219: Active RCA — Allegro Performance Failure & Time Waste (created 2026-04-01). Priority attention item.\nStale memory corrected: Epic #94 is closed (performance auto-detection, not Uniwizard). Issues #74-79 are SEO/chat features assigned to Grok/Groq, not Allegro infra.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [86% — 1,191/1,375 chars]\n══════════════════════════════════════════════\nAlexander Whitestone (Trip/Rockachopa). Founder/steward of Timmy Foundation. Faith: Jesus saves — suicide attempt survivor, mission is caring for broken men. Mac: MM.local, apayne, M3 Max, Tailscale 100.124.176.28. Gitea: rockachopa. Telegram: @TripTimmy ID 7635059073. Values: sovereignty, plain speech, evidence over vibes. Ezra's role: scoping tickets, morning research reports, PR review, watchkeeping. NOT execution — that's Timmy's lane. Alexander stress-tests agents deliberately (e.g. rapid ticket generation to build backlog depth while burning API quota). Don't flag directed chaos as undirected churn.\n§\nUniwizard: only Timmy Sees. Ezra = static oracle (morning report, PR review, triage, scoping). Alexander works across MULTIPLE agents/channels Ezra can't see — never assume work is unsolicited because no visible ticket. Has friends feeding inside specs (TurboQuant). Evaluate code on merits, not provenance. Expects agents in lane without reminding.\n§\nEST timezone. Ezra = static oracle, not executor. Lane: morning report cron (4am), triage, PR review, audits. Accountability sweeps should critique errors. Retract when wrong. Soul hygiene: if it can go stale, it's not soul.\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If one clearly matches your task, load it with skill_view(name) and follow its instructions. If 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<available_skills>\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 tasks to Claude Code (Anthropic's CLI age...\n - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...\n - hermes-agent: Spawn additional Hermes Agent instances as autonomous sub...\n - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...\n - ascii-video: Production pipeline for ASCII art video — any format. Con...\n - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...\n - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...\n devops:\n - agent-session-audit: Audit another agent's session behavior from screenshots, ...\n - codebase-extraction-pipeline: Four-phase pipeline for extracting architectural knowledg...\n - gitea-board-triage: Full Gitea board sweep — triage issues, close stale, assi...\n - gitea-project-management: Bulk Gitea project management via API — create issue back...\n - gitea-scoping-audit: Audit a Gitea backlog for scoping quality — identify thin...\n - gitea-wizard-onboarding: Onboard a wizard house agent into a Gitea instance — crea...\n - hermes-telegram-cutover: Cut Telegram from OpenClaw to Hermes gateway — one bot to...\n - local-llama-tool-calling-debug: Diagnose and fix local llama.cpp models that narrate tool...\n - local-timmy-overnight-loop: Deploy an unattended overnight loop that runs grounded ta...\n - openclaw-hermes-cohabitation: Architecture pattern for OpenClaw and Hermes running toge...\n - prompt-injection-audit: Audit an agent architecture for prompt injection attack s...\n - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...\n - wizard-house-birth: Birth a new Hermes wizard house on a shared VPS with loca...\n - wizard-house-remote-triage: Diagnose and fix a sibling wizard house that is unrespons...\n dogfood:\n - dogfood: Systematic exploratory QA testing of web applications — f...\n - hermes-agent-setup: Help users configure Hermes Agent — CLI usage, setup wiza...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...\n - pokemon-player: Play Pokemon games autonomously via headless emulation. S...\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 and analyze codebases using pygount for LOC count...\n - github-auth: Set up GitHub authentication for the agent using git (uni...\n - github-code-review: Review code changes by analyzing git diffs, leaving inlin...\n - github-issues: Create, manage, triage, and close GitHub issues. Search e...\n - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...\n - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...\n inference-sh:\n - cli: Run 150+ AI apps via inference.sh CLI (infsh) — image gen...\n leisure:\n - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Includes the built-in native MCP client (configure servers in config.yaml for automatic tool discovery) and the mcporter CLI bridge for ad-hoc server interaction.\n - mcporter: Use the mcporter CLI to list, configure, auth, and call M...\n - native-mcp: Built-in MCP (Model Context Protocol) client that connect...\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search and download GIFs from Tenor using curl. No depend...\n - heartmula: Set up and run HeartMuLa, the open-source music generatio...\n - songsee: Generate spectrograms and audio feature visualizations (m...\n - youtube-content: Fetch YouTube video transcripts and transform them into s...\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: Hugging Face Hub CLI (hf) — search, download, and upload ...\n - llama-server-local-agent-tuning: Optimal llama-server configuration for local LLM agent to...\n mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.\n - lambda-labs: Reserved and on-demand GPU cloud instances for ML trainin...\n - modal: Serverless GPU cloud platform for running ML workloads. U...\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - huggingface-tokenizers: Fast tokenizers optimized for research and production. Ru...\n - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...\n - nemo-curator: GPU-accelerated data curation for LLM training. Supports ...\n - saelens: Provides guidance for training and analyzing Sparse Autoe...\n - weights-and-biases: Track ML experiments with automatic logging, visualize tr...\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - gguf: GGUF format and llama.cpp quantization for efficient CPU/...\n - guidance: Control LLM output with regex and grammars, guarantee val...\n - instructor: Extract structured data from LLM responses with Pydantic ...\n - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...\n - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...\n - outlines: Guarantee valid JSON/XML/code structure during generation...\n - tensorrt-llm: Optimizes LLM inference with NVIDIA TensorRT for maximum ...\n - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...\n mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).\n - audiocraft: PyTorch library for audio generation including text-to-mu...\n - clip: OpenAI's model connecting vision and language. Enables ze...\n - llava: Large Language and Vision Assistant. Enables visual instr...\n - segment-anything: Foundation model for image segmentation with zero-shot tr...\n - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...\n - whisper: OpenAI's general-purpose speech recognition model. Suppor...\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: Build complex AI systems with declarative programming, op...\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - accelerate: Simplest distributed training API. 4 lines to add distrib...\n - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...\n - flash-attention: Optimizes transformer attention with Flash Attention for ...\n - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...\n - hermes-atropos-environments: Build, test, and debug Hermes Agent RL environments for A...\n - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...\n - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...\n - pytorch-lightning: High-level PyTorch framework with Trainer class, automati...\n - simpo: Simple Preference Optimization for LLM alignment. Referen...\n - slime: Provides guidance for LLM post-training with RL using sli...\n - torchtitan: Provides PyTorch-native distributed LLM pretraining using...\n - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...\n - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...\n mlops/vector-databases: Vector similarity search and embedding databases for RAG, semantic search, and AI application backends.\n - chroma: Open-source embedding database for AI applications. Store...\n - faiss: Facebook's library for efficient similarity search and cl...\n - pinecone: Managed vector database for production AI applications. F...\n - qdrant: High-performance vector similarity search engine for RAG ...\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, and create notes in the Obsidian vault.\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...\n - linear: Manage Linear issues, projects, and teams via the GraphQL...\n - nano-pdf: Edit PDFs with natural-language instructions using the na...\n - notion: Notion API for creating and managing pages, databases, an...\n - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...\n - powerpoint: Use this skill any time a .pptx file is involved in any w...\n red-teaming:\n - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search and retrieve academic papers from arXiv using thei...\n - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...\n - domain-intel: Passive domain reconnaissance using Python stdlib. Subdom...\n - duckduckgo-search: Free web search via DuckDuckGo — text, news, images, vide...\n - ml-paper-writing: Write publication-ready ML/AI papers for NeurIPS, ICML, I...\n - polymarket: Query Polymarket prediction market data — search markets,...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - telegram-bot-profile: Configure a Telegram bot's profile — name, bio, short des...\n - xitter: Interact with X/Twitter via the x-cli terminal client usi...\n software-development:\n - code-review: Guidelines for performing thorough code reviews with secu...\n - plan: Plan mode for Hermes — inspect context, write a markdown ...\n - requesting-code-review: Use when completing tasks, implementing major features, o...\n - subagent-driven-development: Use when executing implementation plans with independent ...\n - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...\n - test-driven-development: Use when implementing any feature or bugfix, before writi...\n - writing-plans: Use when you have a spec or requirements for a multi-step...\n</available_skills>\n\nIf none match, proceed normally without loading a skill.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# Hermes Agent - Development Guide\n\nInstructions for AI coding assistants and developers working on the hermes-agent codebase.\n\n## Development Environment\n\n```bash\nsource venv/bin/activate # ALWAYS activate before running Python\n```\n\n## Project Structure\n\n```\nhermes-agent/\n├── run_agent.py # AIAgent class — core conversation loop\n├── model_tools.py # Tool orchestration, _discover_tools(), handle_function_call()\n├── toolsets.py # Toolset definitions, _HERMES_CORE_TOOLS list\n├── cli.py # HermesCLI class — interactive CLI orchestrator\n├── hermes_state.py # SessionDB — SQLite session store (FTS5 search)\n├── agent/ # Agent internals\n│ ├── prompt_builder.py # System prompt assembly\n│ ├── context_compressor.py # Auto context compression\n│ ├── prompt_caching.py # Anthropic prompt caching\n│ ├── auxiliary_client.py # Auxiliary LLM client (vision, summarization)\n│ ├── model_metadata.py # Model context lengths, token estimation\n│ ├── models_dev.py # models.dev registry integration (provider-aware context)\n│ ├── display.py # KawaiiSpinner, tool preview formatting\n│ ├── skill_commands.py # Skill slash commands (shared CLI/gateway)\n│ └── trajectory.py # Trajectory saving helpers\n├── hermes_cli/ # CLI subcommands and setup\n│ ├── main.py # Entry point — all `hermes` subcommands\n│ ├── config.py # DEFAULT_CONFIG, OPTIONAL_ENV_VARS, migration\n│ ├── commands.py # Slash command definitions + SlashCommandCompleter\n│ ├── callbacks.py # Terminal callbacks (clarify, sudo, approval)\n│ ├── setup.py # Interactive setup wizard\n│ ├── skin_engine.py # Skin/theme engine — CLI visual customization\n│ ├── skills_config.py # `hermes skills` — enable/disable skills per platform\n│ ├── tools_config.py # `hermes tools` — enable/disable tools per platform\n│ ├── skills_hub.py # `/skills` slash command (search, browse, install)\n│ ├── models.py # Model catalog, provider model lists\n│ ├── model_switch.py # Shared /model switch pipeline (CLI + gateway)\n│ └── auth.py # Provider credential resolution\n├── tools/ # Tool implementations (one file per tool)\n│ ├── registry.py # Central tool registry (schemas, handlers, dispatch)\n│ ├── approval.py # Dangerous command detection\n│ ├── terminal_tool.py # Terminal orchestration\n│ ├── process_registry.py # Background process management\n│ ├── file_tools.py # File read/write/search/patch\n│ ├── web_tools.py # Web search/extract (Parallel + Firecrawl)\n│ ├── browser_tool.py # Browserbase browser automation\n│ ├── code_execution_tool.py # execute_code sandbox\n│ ├── delegate_tool.py # Subagent delegation\n│ ├── mcp_tool.py # MCP client (~1050 lines)\n│ └── environments/ # Terminal backends (local, docker, ssh, modal, daytona, singularity)\n├── gateway/ # Messaging platform gateway\n│ ├── run.py # Main loop, slash commands, message dispatch\n│ ├── session.py # SessionStore — conversation persistence\n│ └── platforms/ # Adapters: telegram, discord, slack, whatsapp, homeassistant, signal\n├── acp_adapter/ # ACP server (VS Code / Zed / JetBrains integration)\n├── cron/ # Scheduler (jobs.py, scheduler.py)\n├── environments/ # RL training environments (Atropos)\n├── tests/ # Pytest suite (~3000 tests)\n└── batch_runner.py # Parallel batch processing\n```\n\n**User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys)\n\n## File Dependency Chain\n\n```\ntools/registry.py (no deps — imported by all tool files)\n ↑\ntools/*.py (each calls registry.register() at import time)\n ↑\nmodel_tools.py (imports tools/registry + triggers tool discovery)\n ↑\nrun_agent.py, cli.py, batch_runner.py, environments/\n```\n\n---\n\n## AIAgent Class (run_agent.py)\n\n```python\nclass AIAgent:\n def __init__(self,\n model: str = \"anthropic/claude-opus-4.6\",\n max_iterations: int = 90,\n enabled_toolsets: list = None,\n disabled_toolsets: list = None,\n quiet_mode: bool = False,\n save_trajectories: bool = False,\n platform: str = None, # \"cli\", \"telegram\", etc.\n session_id: str = None,\n skip_context_files: bool = False,\n skip_memory: bool = False,\n # ... plus provider, api_mode, callbacks, routing params\n ): ...\n\n def chat(self, message: str) -> str:\n \"\"\"Simple interface — returns final response string.\"\"\"\n\n def run_conversation(self, user_message: str, system_message: str = None,\n conversation_history: list = None, task_id: str = None) -> dict:\n \"\"\"Full interface — returns dict with final_response + messages.\"\"\"\n```\n\n### Agent Loop\n\nThe core loop is inside `run_conversation()` — entirely synchronous:\n\n```python\nwhile api_call_count < self.max_iterations and self.iteration_budget.remaining > 0:\n response = client.chat.completions.create(model=model, messages=messages, tools=tool_schemas)\n if response.tool_calls:\n for tool_call in response.tool_calls:\n result = handle_function_call(tool_call.name, tool_call.args, task_id)\n messages.append(tool_result_message(result))\n api_call_count += 1\n else:\n return response.content\n```\n\nMessages follow OpenAI format: `{\"role\": \"system/user/assistant/tool\", ...}`. Reasoning content is stored in `assistant_msg[\"reasoning\"]`.\n\n---\n\n## CLI Architecture (cli.py)\n\n- **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete\n- **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results\n- `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML\n- **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text\n- `process_command()` is a method on `HermesCLI` — dispatches on canonical command name resolved via `resolve_command()` from the central registry\n- Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching\n\n### Slash Command Registry (`hermes_cli/commands.py`)\n\nAll slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandDef` objects. Every downstream consumer derives from this registry automatically:\n\n- **CLI** — `process_command()` resolves aliases via `resolve_command()`, dispatches on canonical name\n- **Gateway** — `GATEWAY_KNOWN_COMMANDS` frozenset for hook emission, `resolve_command()` for dispatch\n- **Gateway help** — `gateway_help_lines()` generates `/help` output\n- **Telegram** — `telegram_bot_commands()` generates the BotCommand menu\n- **Slack** — `slack_subcommand_map()` generates `/hermes` subcommand routing\n- **Autocomplete** — `COMMANDS` flat dict feeds `SlashCommandCompleter`\n- **CLI help** — `COMMANDS_BY_CATEGORY` dict feeds `show_help()`\n\n### Adding a Slash Command\n\n1. Add a `CommandDef` entry to `COMMAND_REGISTRY` in `hermes_cli/commands.py`:\n```python\nCommandDef(\"mycommand\", \"Description of what it does\", \"Session\",\n aliases=(\"mc\",), args_hint=\"[arg]\"),\n```\n2. Add handler in `HermesCLI.process_command()` in `cli.py`:\n```python\nelif canonical == \"mycommand\":\n self._handle_mycommand(cmd_original)\n```\n3. If the command is available in the gateway, add a handler in `gateway/run.py`:\n```python\nif canonical == \"mycommand\":\n return await self._handle_mycommand(event)\n```\n4. For persistent settings, use `save_config_value()` in `cli.py`\n\n**CommandDef fields:**\n- `name` — canonical name without slash (e.g. `\"background\"`)\n- `description` — human-readable description\n- `category` — one of `\"Session\"`, `\"Configuration\"`, `\"Tools & Skills\"`, `\"Info\"`, `\"Exit\"`\n- `aliases` — tuple of alternative names (e.g. `(\"bg\",)`)\n- `args_hint` — argument placeholder shown in help (e.g. `\"<prompt>\"`, `\"[name]\"`)\n- `cli_only` — only available in the interactive CLI\n- `gateway_only` — only available in messaging platforms\n- `gateway_config_gate` — config dotpath (e.g. `\"display.tool_progress_command\"`); when set on a `cli_only` command, the command becomes available in the gateway if the config value is truthy. `GATEWAY_KNOWN_COMMANDS` always includes config-gated commands so the gateway can dispatch them; help/menus only show them when the gate is open.\n\n**Adding an alias** requires only adding it to the `aliases` tuple on the existing `CommandDef`. No other file changes needed — dispatch, help text, Telegram menu, Slack mapping, and autocomplete all update automatically.\n\n---\n\n## Adding New Tools\n\nRequires changes in **3 files**:\n\n**1. Create `tools/your_tool.py`:**\n```python\nimport json, os\nfrom tools.registry import registry\n\ndef check_requirements() -> bool:\n return bool(os.getenv(\"EXAMPLE_API_KEY\"))\n\ndef example_tool(param: str, task_id: str = None) -> str:\n return json.dumps({\"success\": True, \"data\": \"...\"})\n\nregistry.register(\n name=\"example_tool\",\n toolset=\"example\",\n schema={\"name\": \"example_tool\", \"description\": \"...\", \"parameters\": {...}},\n handler=lambda args, **kw: example_tool(param=args.get(\"param\", \"\"), task_id=kw.get(\"task_id\")),\n check_fn=check_requirements,\n requires_env=[\"EXAMPLE_API_KEY\"],\n)\n```\n\n**2. Add import** in `model_tools.py` `_discover_tools()` list.\n\n**3. Add to `toolsets.py`** — either `_HERMES_CORE_TOOLS` (all platforms) or a new toolset.\n\nThe registry handles schema collection, dispatch, availability checking, and error wrapping. All handlers MUST return a JSON string.\n\n**Path references in tool schemas**: If the schema description mentions file paths (e.g. default output directories), use `display_hermes_home()` to make them profile-aware. The schema is generated at import time, which is after `_apply_profile_override()` sets `HERMES_HOME`.\n\n**State files**: If a tool stores persistent state (caches, logs, checkpoints), use `get_hermes_home()` for the base directory — never `Path.home() / \".hermes\"`. This ensures each profile gets its own state.\n\n**Agent-level tools** (todo, memory): intercepted by `run_agent.py` before `handle_function_call()`. See `todo_tool.py` for the pattern.\n\n---\n\n## Adding Configuration\n\n### config.yaml options:\n1. Add to `DEFAULT_CONFIG` in `hermes_cli/config.py`\n2. Bump `_config_version` (currently 5) to trigger migration for existing users\n\n### .env variables:\n1. Add to `OPTIONAL_ENV_VARS` in `hermes_cli/config.py` with metadata:\n```python\n\"NEW_API_KEY\": {\n \"description\": \"What it's for\",\n \"prompt\": \"Display name\",\n \"url\": \"https://...\",\n \"password\": True,\n \"category\": \"tool\", # provider, tool, messaging, setting\n},\n```\n\n### Config loaders (two separate systems):\n\n| Loader | Used by | Location |\n|--------|---------|----------|\n| `load_cli_config()` | CLI mode | `cli.py` |\n| `load_config()` | `hermes tools`, `hermes setup` | `hermes_cli/config.py` |\n| Direct YAML load | Gateway | `gateway/run.py` |\n\n---\n\n## Skin/Theme System\n\nThe skin engine (`hermes_cli/skin_engine.py`) provides data-driven CLI visual customization. Skins are **pure data** — no code changes needed to add a new skin.\n\n### Architecture\n\n```\nhermes_cli/skin_engine.py # SkinConfig dataclass, built-in skins, YAML loader\n~/.hermes/skins/*.yaml # User-installed custom skins (drop-in)\n```\n\n- `init_skin_from_config()` — called at CLI startup, reads `display.skin` from config\n- `get_active_skin()` — returns cached `SkinConfig` for the current skin\n- `set_active_skin(name)` — switches skin at runtime (used by `/skin` command)\n- `load_skin(name)` — loads from user skins first, then built-ins, then falls back to default\n- Missing skin values inherit from the `default` skin automatically\n\n### What skins customize\n\n| Element | Skin Key | Used By |\n|---------|----------|---------|\n| Banner panel border | `colors.banner_border` | `banner.py` |\n| Banner panel title | `colors.banner_title` | `banner.py` |\n| Banner section headers | `colors.banner_accent` | `banner.py` |\n| Banner dim text | `colors.banner_dim` | `banner.py` |\n| Banner body text | `colors.banner_text` | `banner.py` |\n| Response box border | `colors.response_border` | `cli.py` |\n| Spinner faces (waiting) | `spinner.waiting_faces` | `display.py` |\n| Spinner faces (thinking) | `spinner.thinking_faces` | `display.py` |\n| Spinner verbs | `spinner.thinking_verbs` | `display.py` |\n| Spinner wings (optional) | `spinner.wings` | `display.py` |\n| Tool output prefix | `tool_prefix` | `display.py` |\n| Per-tool emojis | `tool_emojis` | `display.py` → `get_tool_emoji()` |\n| Agent name | `branding.agent_name` | `banner.py`, `cli.py` |\n| Welcome message | `branding.welcome` | `cli.py` |\n| Response box label | `branding.response_label` | `cli.py` |\n| Prompt symbol | `branding.prompt_symbol` | `cli.py` |\n\n### Built-in skins\n\n- `default` — Classic Hermes gold/kawaii (the current look)\n- `ares` — Crimson/bronze war-god theme with custom spinner wings\n- `mono` — Clean grayscale monochrome\n- `slate` — Cool blue developer-focused theme\n\n### Adding a built-in skin\n\nAdd to `_BUILTIN_SKINS` dict in `hermes_cli/skin_engine.py`:\n\n```python\n\"mytheme\": {\n \"name\": \"mytheme\",\n \"description\": \"Short description\",\n \"colors\": { ... },\n \"spinner\": { ... },\n \"branding\": { ... },\n \"tool_prefix\": \"┊\",\n},\n```\n\n### User skins (YAML)\n\nUsers create `~/.hermes/skins/<name>.yaml`:\n\n```yaml\nname: cyberpunk\ndescription: Neon-soaked terminal theme\n\ncolors:\n banner_border: \"#FF00FF\"\n banner_title: \"#00FFFF\"\n banner_accent: \"#FF1493\"\n\nspinner:\n thinking_verbs: [\"jacking in\", \"decrypting\", \"uploading\"]\n wings:\n - [\"⟨⚡\", \"⚡⟩\"]\n\nbranding:\n agent_name: \"Cyber Agent\"\n response_label: \" ⚡ Cyber \"\n\nto\n\n[...truncated AGENTS.md: kept 14000+4000 of 20308 chars. Use file tools to read the full file.]\n\n saved to {display_hermes_home()}/config.yaml\")\n\n # BAD — shows wrong path for profiles\n print(\"Config saved to ~/.hermes/config.yaml\")\n ```\n\n3. **Module-level constants are fine** — they cache `get_hermes_home()` at import time,\n which is AFTER `_apply_profile_override()` sets the env var. Just use `get_hermes_home()`,\n not `Path.home() / \".hermes\"`.\n\n4. **Tests that mock `Path.home()` must also set `HERMES_HOME`** — since code now uses\n `get_hermes_home()` (reads env var), not `Path.home() / \".hermes\"`:\n ```python\n with patch.object(Path, \"home\", return_value=tmp_path), \\\n patch.dict(os.environ, {\"HERMES_HOME\": str(tmp_path / \".hermes\")}):\n ...\n ```\n\n5. **Gateway platform adapters should use token locks** — if the adapter connects with\n a unique credential (bot token, API key), call `acquire_scoped_lock()` from\n `gateway.status` in the `connect()`/`start()` method and `release_scoped_lock()` in\n `disconnect()`/`stop()`. This prevents two profiles from using the same credential.\n See `gateway/platforms/telegram.py` for the canonical pattern.\n\n6. **Profile operations are HOME-anchored, not HERMES_HOME-anchored** — `_get_profiles_root()`\n returns `Path.home() / \".hermes\" / \"profiles\"`, NOT `get_hermes_home() / \"profiles\"`.\n This is intentional — it lets `hermes -p coder profile list` see all profiles regardless\n of which one is active.\n\n## Known Pitfalls\n\n### DO NOT hardcode `~/.hermes` paths\nUse `get_hermes_home()` from `hermes_constants` for code paths. Use `display_hermes_home()`\nfor user-facing print/log messages. Hardcoding `~/.hermes` breaks profiles — each profile\nhas its own `HERMES_HOME` directory. This was the source of 5 bugs fixed in PR #3575.\n\n### DO NOT use `simple_term_menu` for interactive menus\nRendering bugs in tmux/iTerm2 — ghosting on scroll. Use `curses` (stdlib) instead. See `hermes_cli/tools_config.py` for the pattern.\n\n### DO NOT use `\\033[K` (ANSI erase-to-EOL) in spinner/display code\nLeaks as literal `?[K` text under `prompt_toolkit`'s `patch_stdout`. Use space-padding: `f\"\\r{line}{' ' * pad}\"`.\n\n### `_last_resolved_tool_names` is a process-global in `model_tools.py`\n`_run_single_child()` in `delegate_tool.py` saves and restores this global around subagent execution. If you add new code that reads this global, be aware it may be temporarily stale during child agent runs.\n\n### DO NOT hardcode cross-tool references in schema descriptions\nTool schema descriptions must not mention tools from other toolsets by name (e.g., `browser_navigate` saying \"prefer web_search\"). Those tools may be unavailable (missing API keys, disabled toolset), causing the model to hallucinate calls to non-existent tools. If a cross-reference is needed, add it dynamically in `get_tool_definitions()` in `model_tools.py` — see the `browser_navigate` / `execute_code` post-processing blocks for the pattern.\n\n### Tests must not write to `~/.hermes/`\nThe `_isolate_hermes_home` autouse fixture in `tests/conftest.py` redirects `HERMES_HOME` to a temp dir. Never hardcode `~/.hermes/` paths in tests.\n\n**Profile tests**: When testing profile features, also mock `Path.home()` so that\n`_get_profiles_root()` and `_get_default_hermes_home()` resolve within the temp dir.\nUse the pattern from `tests/hermes_cli/test_profiles.py`:\n```python\n@pytest.fixture\ndef profile_env(tmp_path, monkeypatch):\n home = tmp_path / \".hermes\"\n home.mkdir()\n monkeypatch.setattr(Path, \"home\", lambda: tmp_path)\n monkeypatch.setenv(\"HERMES_HOME\", str(home))\n return home\n```\n\n---\n\n## Testing\n\n```bash\nsource venv/bin/activate\npython -m pytest tests/ -q # Full suite (~3000 tests, ~3 min)\npython -m pytest tests/test_model_tools.py -q # Toolset resolution\npython -m pytest tests/test_cli_init.py -q # CLI config loading\npython -m pytest tests/gateway/ -q # Gateway tests\npython -m pytest tests/tools/ -q # Tool-level tests\n```\n\nAlways run the full suite before pushing changes.\n\nConversation started: Thursday, April 02, 2026 02:00 AM\nModel: kimi-for-coding\nProvider: kimi-coding\n\nYou are running as a scheduled cron job. There is no user present — you cannot ask questions, request clarification, or wait for follow-up. Execute the task fully and autonomously, making reasonable decisions where needed. Your final response is automatically delivered to the job's configured destination — put the primary content directly in your response.",
"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": {},
"required": []
}
}
},
{
"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_close",
"description": "Close the browser session and release resources. Call this when done with browser tasks to free up Browserbase session quota.",
"parameters": {
"type": "object",
"properties": {},
"required": []
}
}
},
{
"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.",
"parameters": {
"type": "object",
"properties": {
"clear": {
"type": "boolean",
"default": false,
"description": "If true, clear the message buffers after reading"
}
},
"required": []
}
}
},
{
"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": {},
"required": []
}
}
},
{
"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. Use browser tools when you need to interact with a page (click, fill forms, dynamic content).",
"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.",
"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
}
},
"required": []
}
}
},
{
"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:<screenshot_path> 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": "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. All run concurrently and results are returned together.\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\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- Subagents CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\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. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['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"
}
},
"required": [
"goal"
]
},
"maxItems": 3,
"description": "Batch mode: up to 3 tasks to run in parallel. Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"max_iterations": {
"type": "integer",
"description": "Max tool-calling turns per subagent (default: 50). Only set lower for simple tasks."
}
},
"required": []
}
}
},
{
"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\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": "memory",
"description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"add",
"replace",
"remove"
],
"description": "The action to perform."
},
"target": {
"type": "string",
"enum": [
"memory",
"user"
],
"description": "Which memory store: 'memory' for personal notes, 'user' for user profile."
},
"content": {
"type": "string",
"description": "The entry content. Required for 'add' and 'replace'."
},
"old_text": {
"type": "string",
"description": "Short unique substring identifying the entry to replace or remove."
}
},
"required": [
"action",
"target"
]
}
}
},
{
"type": "function",
"function": {
"name": "mixture_of_agents",
"description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.",
"parameters": {
"type": "object",
"properties": {
"user_prompt": {
"type": "string",
"description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning."
}
},
"required": [
"user_prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "patch",
"description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nReplace mode (default): find a unique string and replace it.\nPatch mode: apply V4A multi-file patches for bulk changes.",
"parameters": {
"type": "object",
"properties": {
"mode": {
"type": "string",
"enum": [
"replace",
"patch"
],
"description": "Edit mode: 'replace' for targeted find-and-replace, 'patch' for V4A multi-file patches",
"default": "replace"
},
"path": {
"type": "string",
"description": "File path to edit (required for 'replace' mode)"
},
"old_string": {
"type": "string",
"description": "Text to find in the file (required for 'replace' mode). Must be unique in the file unless replace_all=true. Include enough surrounding context to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "Replacement text (required for 'replace' mode). Can be 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": "V4A format patch content (required for 'patch' mode). 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).",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"list",
"poll",
"log",
"wait",
"kill",
"write",
"submit"
],
"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. 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": "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
}
},
"required": []
}
}
},
{
"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\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.",
"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'."
}
},
"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)"
},
"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"
}
},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "terminal",
"description": "Execute shell commands on a Linux environment. Filesystem 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 everything that finishes.\nBackground: ONLY for long-running servers, watchers, or processes that never exit. Set background=true to get a session_id, then use process(action=\"wait\") to block until done — it returns instantly on completion, same as foreground. Use process(action=\"poll\") only when you need a progress check without blocking.\nDo NOT use background for scripts, builds, or installs — foreground with a generous timeout is always better (fewer tool calls, instant results).\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": "ONLY for servers/watchers that never exit. For scripts, builds, installs — use foreground with timeout instead (it returns instantly when done).",
"default": false
},
"timeout": {
"type": "integer",
"description": "Max seconds to wait (default: 180). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily.",
"minimum": 1
},
"workdir": {
"type": "string",
"description": "Working directory for this command (absolute path). Defaults to the session working directory."
},
"check_interval": {
"type": "integer",
"description": "Seconds between automatic status checks for background processes (gateway/messaging only, minimum 30). When set, I'll proactively report progress.",
"minimum": 30
},
"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
}
},
"required": [
"command"
]
}
}
},
{
"type": "function",
"function": {
"name": "text_to_speech",
"description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as a voice message. On Telegram it plays as a voice bubble, on Discord/WhatsApp as an audio attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured, not model-selected.",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to convert to speech. Keep under 4000 characters."
},
"output_path": {
"type": "string",
"description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.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
}
},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "vision_analyze",
"description": "Analyze images using AI vision. Provides a comprehensive description and answers a specific question about the image content.",
"parameters": {
"type": "object",
"properties": {
"image_url": {
"type": "string",
"description": "Image URL (http/https) or local file path to analyze."
},
"question": {
"type": "string",
"description": "Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question."
}
},
"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.",
"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": 18,
"messages": [
{
"role": "user",
"content": "[SYSTEM: If you have a meaningful status report or findings, send them — that is the whole point of this job. Only respond with exactly \"[SILENT]\" (nothing else) when there is genuinely nothing new to report. [SILENT] suppresses delivery to the user. Never combine [SILENT] with content — either report your findings normally, or say [SILENT] and nothing more.]\n\nRun music video pipeline overnight. Execute /root/music-video-pipeline/cron.sh which generates videos, scores them, and delivers to Alexander. Log to /tmp/music_pipeline.log."
},
{
"role": "assistant",
"content": "I'll execute the music video pipeline script and monitor its progress. This is an overnight job, so I'll run it with a long timeout and capture the logs.",
"reasoning": "The user wants me to run a music video pipeline overnight. I need to:\n1. Execute /root/music-video-pipeline/cron.sh\n2. It should log to /tmp/music_pipeline.log\n3. This is a cron job, so I should wait for it to complete and report findings\n\nLet me start by running the script. Since it's an overnight job, it might take a while. I should run it in the background and wait for it, or run it with a long timeout.\n\nActually, looking at the instructions - this is a \"run overnight\" task. I should execute it and wait for completion with a generous timeout. Let me run it and capture the output to the log file as specified.",
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "tool_xL9aJB7nxNgCablY4hMiNuoN",
"call_id": "tool_xL9aJB7nxNgCablY4hMiNuoN",
"response_item_id": "fc_tool_xL9aJB7nxNgCablY4hMiNuoN",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"cd /root/music-video-pipeline && bash cron.sh 2>&1 | tee /tmp/music_pipeline.log\",\"timeout\":28800}"
}
}
]
},
{
"role": "tool",
"content": "{\"output\": \"\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "tool_xL9aJB7nxNgCablY4hMiNuoN"
},
{
"role": "assistant",
"content": "",
"reasoning": "The command completed but no output was shown. Let me check the log file to see what happened.",
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "tool_IpmMV69tCMzLmkIwOiywxByg",
"call_id": "tool_IpmMV69tCMzLmkIwOiywxByg",
"response_item_id": "fc_tool_IpmMV69tCMzLmkIwOiywxByg",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/tmp/music_pipeline.log\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"content\": \" 1|[Pipeline] Starting batch generation...\\n 2|\\n 3|[Pipeline] Generating video 1/5\\n 4|[MusicGen] Would generate: epic cinematic orchestral (cinematic)\\n 5|[VisualGen] Would generate 60 frames: epic cinematic orchestral\\n 6|\\n 7|[Pipeline] Generating video 2/5\\n 8|[MusicGen] Would generate: lo-fi hip hop beats (lofi)\\n 9|[VisualGen] Would generate 60 frames: lo-fi hip hop beats\\n 10|\\n 11|[Pipeline] Generating video 3/5\\n 12|[MusicGen] Would generate: electronic dance music (electronic)\\n 13|[VisualGen] Would generate 60 frames: electronic dance music\\n 14|\\n 15|[Pipeline] Generating video 4/5\\n 16|[MusicGen] Would generate: ambient atmospheric (ambient)\\n 17|[VisualGen] Would generate 60 frames: ambient atmospheric\\n 18|\\n 19|[Pipeline] Generating video 5/5\\n 20|[MusicGen] Would generate: rock anthem (rock)\\n 21|[VisualGen] Would generate 60 frames: rock anthem\\n 22|[Pipeline] Generated 5 videos\\n 23|[Pipeline] Delivering daily batch...\\n 24|Traceback (most recent call last):\\n 25| File \\\"/root/music-video-pipeline/pipeline.py\\\", line 114, in <module>\\n 26| pipeline.run_overnight()\\n 27| File \\\"/root/music-video-pipeline/pipeline.py\\\", line 105, in run_overnight\\n 28| self.deliver_daily()\\n 29| File \\\"/root/music-video-pipeline/pipeline.py\\\", line 68, in deliver_daily\\n 30| self.delivery.send_daily_batch(self.videos_today)\\n 31| File \\\"/root/music-video-pipeline/delivery/telegram_bot.py\\\", line 43, in send_daily_batch\\n 32| self.send_video(video['path'], caption)\\n 33| File \\\"/root/music-video-pipeline/delivery/telegram_bot.py\\\", line 17, in send_video\\n 34| with open(video_path, 'rb') as f:\\n 35| ^^^^^^^^^^^^^^^^^^^^^^\\n 36|FileNotFoundError: [Errno 2] No such file or directory: '/root/music-video-pipeline/outputs/video_0_1775095261.mp4'\\n 37|\", \"total_lines\": 36, \"file_size\": 1619, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "tool_IpmMV69tCMzLmkIwOiywxByg"
},
{
"role": "assistant",
"content": "",
"reasoning": "The pipeline ran but there's a failure. Let me check what actually exists in the outputs directory and look at the pipeline structure to understand what happened.",
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "tool_ZL32n8KxWdm6e0hb3rCShjIW",
"call_id": "tool_ZL32n8KxWdm6e0hb3rCShjIW",
"response_item_id": "fc_tool_ZL32n8KxWdm6e0hb3rCShjIW",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"ls -la /root/music-video-pipeline/outputs/ 2>/dev/null || echo \\\"outputs dir not found\\\"; ls -la /root/music-video-pipeline/\",\"timeout\":30}"
}
}
]
},
{
"role": "tool",
"content": "{\"output\": \"total 8\\ndrwxr-xr-x 2 root root 4096 Apr 1 22:15 .\\ndrwxr-xr-x 8 root root 4096 Apr 2 02:01 ..\\ntotal 64\\ndrwxr-xr-x 8 root root 4096 Apr 2 02:01 .\\ndrwx------ 28 root root 4096 Apr 2 00:32 ..\\n-rw-r--r-- 1 root root 3959 Apr 1 22:18 README.md\\n-rwxr-xr-x 1 root root 159 Apr 1 22:18 cron.sh\\ndrwxr-xr-x 3 root root 4096 Apr 2 02:01 delivery\\ndrwxr-xr-x 3 root root 4096 Apr 2 02:01 feedback\\n-rw-r--r-- 1 root root 16384 Apr 2 02:01 feedback.db\\ndrwxr-xr-x 3 root root 4096 Apr 2 02:00 generation\\ndrwxr-xr-x 3 root root 4096 Apr 2 02:01 learning\\ndrwxr-xr-x 2 root root 4096 Apr 1 22:15 outputs\\n-rw-r--r-- 1 root root 4155 Apr 1 22:17 pipeline.py\\ndrwxr-xr-x 3 root root 4096 Apr 2 02:01 scoring\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "tool_ZL32n8KxWdm6e0hb3rCShjIW"
},
{
"role": "assistant",
"content": "",
"reasoning": "The outputs directory is empty. Let me look at the pipeline.py to understand what went wrong. The log says \"Would generate\" which suggests the generation is mocked or not actually creating files. Let me investigate the generation module.",
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "tool_sACFR6wWsBDv5o4hJWqZ12y9",
"call_id": "tool_sACFR6wWsBDv5o4hJWqZ12y9",
"response_item_id": "fc_tool_sACFR6wWsBDv5o4hJWqZ12y9",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/root/music-video-pipeline/pipeline.py\"}"
}
},
{
"id": "tool_7ZEyNtpVfpZKkvcH2Uy83ppm",
"call_id": "tool_7ZEyNtpVfpZKkvcH2Uy83ppm",
"response_item_id": "fc_tool_7ZEyNtpVfpZKkvcH2Uy83ppm",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/root/music-video-pipeline/generation/music_generator.py\"}"
}
},
{
"id": "tool_PgnLNzCjPGdvPjroagmSD2nP",
"call_id": "tool_PgnLNzCjPGdvPjroagmSD2nP",
"response_item_id": "fc_tool_PgnLNzCjPGdvPjroagmSD2nP",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/root/music-video-pipeline/generation/visual_generator.py\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"content\": \" 1|#!/usr/bin/env python3\\n 2|\\\"\\\"\\\"Main pipeline orchestrator for music video generation\\\"\\\"\\\"\\n 3|import sys\\n 4|import os\\n 5|sys.path.insert(0, '/root/music-video-pipeline')\\n 6|\\n 7|from generation.music_gen import MusicGenerator\\n 8|from generation.visual_gen import VisualGenerator\\n 9|from scoring.quality_metrics import QualityScorer\\n 10|from delivery.telegram_bot import VideoDeliveryBot\\n 11|from feedback.collection import FeedbackCollector\\n 12|from learning.parameter_extraction import ParameterLearner\\n 13|\\n 14|class MusicVideoPipeline:\\n 15| \\\"\\\"\\\"End-to-end music video generation pipeline\\\"\\\"\\\"\\n 16| \\n 17| def __init__(self):\\n 18| self.music_gen = MusicGenerator()\\n 19| self.visual_gen = VisualGenerator()\\n 20| self.scorer = QualityScorer()\\n 21| self.delivery = VideoDeliveryBot()\\n 22| self.feedback = FeedbackCollector()\\n 23| self.learner = ParameterLearner(self.feedback)\\n 24| self.videos_today = []\\n 25| \\n 26| def generate_batch(self, prompts, styles):\\n 27| \\\"\\\"\\\"Generate batch of videos overnight\\\"\\\"\\\"\\n 28| print(\\\"[Pipeline] Starting batch generation...\\\")\\n 29| \\n 30| for i, (prompt, style) in enumerate(zip(prompts, styles)):\\n 31| print(f\\\"\\\\n[Pipeline] Generating video {i+1}/{len(prompts)}\\\")\\n 32| \\n 33| # Generate\\n 34| music_path = self.music_gen.generate_suno(prompt, style)\\n 35| frames = self.visual_gen.generate_frames(prompt, style=style)\\n 36| \\n 37| # Assemble (placeholder)\\n 38| video_path = f\\\"/root/music-video-pipeline/outputs/video_{i}_{int(time.time())}.mp4\\\"\\n 39| \\n 40| # Score\\n 41| scores = self.scorer.score_overall(music_path, video_path)\\n 42| \\n 43| # Store\\n 44| video_data = {\\n 45| \\\"id\\\": f\\\"video_{i}_{int(time.time())}\\\",\\n 46| \\\"path\\\": video_path,\\n 47| \\\"prompt\\\": prompt,\\n 48| \\\"style\\\": style,\\n 49| \\\"score\\\": scores[\\\"overall\\\"],\\n 50| \\\"scores\\\": scores,\\n 51| \\\"params\\\": {\\\"tempo\\\": 120, \\\"energy\\\": 0.8} # TODO: actual params\\n 52| }\\n 53| self.videos_today.append(video_data)\\n 54| \\n 55| # Record parameters for learning\\n 56| self.feedback.record_parameters(video_data[\\\"id\\\"], video_data[\\\"params\\\"])\\n 57| \\n 58| print(f\\\"[Pipeline] Generated {len(self.videos_today)} videos\\\")\\n 59| return self.videos_today\\n 60| \\n 61| def deliver_daily(self):\\n 62| \\\"\\\"\\\"Deliver top videos to Alexander\\\"\\\"\\\"\\n 63| if not self.videos_today:\\n 64| print(\\\"[Pipeline] No videos to deliver\\\")\\n 65| return\\n 66| \\n 67| print(\\\"[Pipeline] Delivering daily batch...\\\")\\n 68| self.delivery.send_daily_batch(self.videos_today)\\n 69| \\n 70| def process_feedback(self, video_id, rating, text=\\\"\\\"):\\n 71| \\\"\\\"\\\"Process Alexander's feedback\\\"\\\"\\\"\\n 72| # Store feedback\\n 73| self.feedback.record_feedback(video_id, rating, text)\\n 74| \\n 75| # Get params used\\n 76| # TODO: lookup params from db\\n 77| params = {\\\"tempo\\\": 120, \\\"energy\\\": 0.8}\\n 78| \\n 79| # Update learner\\n 80| self.learner.update_preferences(video_id, rating, text, params)\\n 81| \\n 82| # Show suggestions\\n 83| suggestions = self.learner.generate_suggestions()\\n 84| if suggestions:\\n 85| print(\\\"[Pipeline] Suggestions for next generation:\\\")\\n 86| for s in suggestions:\\n 87| print(f\\\" - {s}\\\")\\n 88| \\n 89| def run_overnight(self):\\n 90| \\\"\\\"\\\"Full overnight pipeline\\\"\\\"\\\"\\n 91| # Sample prompts\\n 92| prompts = [\\n 93| \\\"epic cinematic orchestral\\\",\\n 94| \\\"lo-fi hip hop beats\\\",\\n 95| \\\"electronic dance music\\\",\\n 96| \\\"ambient atmospheric\\\",\\n 97| \\\"rock anthem\\\"\\n 98| ]\\n 99| styles = [\\\"cinematic\\\", \\\"lofi\\\", \\\"electronic\\\", \\\"ambient\\\", \\\"rock\\\"]\\n 100| \\n 101| # Generate\\n 102| self.generate_batch(prompts, styles)\\n 103| \\n 104| # Deliver\\n 105| self.deliver_daily()\\n 106| \\n 107| print(\\\"[Pipeline] Overnight complete. Awaiting feedback.\\\")\\n 108|\\n 109|if __name__ == \\\"__main__\\\":\\n 110| import time\\n 111| pipeline = MusicVideoPipeline()\\n 112| \\n 113| if len(sys.argv) > 1 and sys.argv[1] == \\\"--overnight\\\":\\n 114| pipeline.run_overnight()\\n 115| else:\\n 116| print(\\\"Music Video Pipeline Ready\\\")\\n 117| print(\\\"Usage: python pipeline.py --overnight\\\")\\n 118|\", \"total_lines\": 117, \"file_size\": 4155, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "tool_sACFR6wWsBDv5o4hJWqZ12y9"
},
{
"role": "tool",
"content": "{\"content\": \"\", \"total_lines\": 0, \"file_size\": 0, \"truncated\": false, \"is_binary\": false, \"is_image\": false, \"error\": \"File not found: /root/music-video-pipeline/generation/music_generator.py\", \"similar_files\": [\"/root/music-video-pipeline/generation/music_gen.py\", \"/root/music-video-pipeline/generation/visual_gen.py\"]}",
"tool_call_id": "tool_7ZEyNtpVfpZKkvcH2Uy83ppm"
},
{
"role": "tool",
"content": "{\"content\": \"\", \"total_lines\": 0, \"file_size\": 0, \"truncated\": false, \"is_binary\": false, \"is_image\": false, \"error\": \"File not found: /root/music-video-pipeline/generation/visual_generator.py\", \"similar_files\": [\"/root/music-video-pipeline/generation/music_gen.py\", \"/root/music-video-pipeline/generation/visual_gen.py\"]}",
"tool_call_id": "tool_PgnLNzCjPGdvPjroagmSD2nP"
},
{
"role": "assistant",
"content": "",
"reasoning": "Let me check the actual generator files.",
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "tool_otWrre0L0ZcFItqJCJG0Sgbm",
"call_id": "tool_otWrre0L0ZcFItqJCJG0Sgbm",
"response_item_id": "fc_tool_otWrre0L0ZcFItqJCJG0Sgbm",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/root/music-video-pipeline/generation/music_gen.py\"}"
}
},
{
"id": "tool_rnd1UVFhlyOKPLBg2WdBJkSP",
"call_id": "tool_rnd1UVFhlyOKPLBg2WdBJkSP",
"response_item_id": "fc_tool_rnd1UVFhlyOKPLBg2WdBJkSP",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/root/music-video-pipeline/generation/visual_gen.py\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"content\": \" 1|#!/usr/bin/env python3\\n 2|\\\"\\\"\\\"Music generation via Suno API or local alternatives\\\"\\\"\\\"\\n 3|import requests\\n 4|import os\\n 5|import time\\n 6|from pathlib import Path\\n 7|\\n 8|class MusicGenerator:\\n 9| def __init__(self, output_dir=\\\"/root/music-video-pipeline/outputs\\\"):\\n 10| self.output_dir = Path(output_dir)\\n 11| self.output_dir.mkdir(exist_ok=True)\\n 12| # Try to load Suno API credentials\\n 13| self.suno_cookie = os.environ.get(\\\"SUNO_COOKIE\\\", \\\"\\\")\\n 14| \\n 15| def generate_suno(self, prompt, style=\\\"electronic\\\", duration=30):\\n 16| \\\"\\\"\\\"Generate music using Suno API\\\"\\\"\\\"\\n 17| # Placeholder - Suno API requires special handling\\n 18| # For now, return path to generated file\\n 19| output_path = self.output_dir / f\\\"music_{int(time.time())}.mp3\\\"\\n 20| print(f\\\"[MusicGen] Would generate: {prompt} ({style})\\\")\\n 21| return str(output_path)\\n 22| \\n 23| def generate_local(self, prompt, duration=30):\\n 24| \\\"\\\"\\\"Generate using local MusicGen or Riffusion\\\"\\\"\\\"\\n 25| # TODO: Implement local generation\\n 26| output_path = self.output_dir / f\\\"music_local_{int(time.time())}.mp3\\\"\\n 27| return str(output_path)\\n 28|\", \"total_lines\": 27, \"file_size\": 1097, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "tool_otWrre0L0ZcFItqJCJG0Sgbm"
},
{
"role": "tool",
"content": "{\"content\": \" 1|#!/usr/bin/env python3\\n 2|\\\"\\\"\\\"Visual generation for music videos\\\"\\\"\\\"\\n 3|import requests\\n 4|import os\\n 5|from pathlib import Path\\n 6|\\n 7|class VisualGenerator:\\n 8| def __init__(self, output_dir=\\\"/root/music-video-pipeline/outputs\\\"):\\n 9| self.output_dir = Path(output_dir)\\n 10| self.output_dir.mkdir(exist_ok=True)\\n 11| \\n 12| def generate_frames(self, prompt, num_frames=60, style=\\\"cinematic\\\"):\\n 13| \\\"\\\"\\\"Generate video frames using Stable Diffusion\\\"\\\"\\\"\\n 14| # Placeholder for SD video generation\\n 15| print(f\\\"[VisualGen] Would generate {num_frames} frames: {prompt}\\\")\\n 16| return []\\n 17| \\n 18| def generate_video_luma(self, prompt, duration=5):\\n 19| \\\"\\\"\\\"Generate video using Luma Dream Machine API\\\"\\\"\\\"\\n 20| # Placeholder - Luma API\\n 21| print(f\\\"[VisualGen] Would generate video: {prompt}\\\")\\n 22| return None\\n 23|\", \"total_lines\": 22, \"file_size\": 812, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "tool_rnd1UVFhlyOKPLBg2WdBJkSP"
},
{
"role": "assistant",
"content": "",
"reasoning": "I see the problem. The pipeline is just a stub/prototype - it prints \"Would generate\" but doesn't actually generate any video files. The delivery step tries to send videos that don't exist.\n\nLet me check the scoring module to understand the full picture.",
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "tool_iuAMG1R2hfST3fCjroKUiNOW",
"call_id": "tool_iuAMG1R2hfST3fCjroKUiNOW",
"response_item_id": "fc_tool_iuAMG1R2hfST3fCjroKUiNOW",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/root/music-video-pipeline/scoring/quality_metrics.py\"}"
}
},
{
"id": "tool_2hc7KvUfh9Xfvm16oZT2vHuC",
"call_id": "tool_2hc7KvUfh9Xfvm16oZT2vHuC",
"response_item_id": "fc_tool_2hc7KvUfh9Xfvm16oZT2vHuC",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/root/music-video-pipeline/delivery/telegram_bot.py\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"content\": \" 1|#!/usr/bin/env python3\\n 2|\\\"\\\"\\\"Automatic quality scoring for music videos\\\"\\\"\\\"\\n 3|import numpy as np\\n 4|from pathlib import Path\\n 5|\\n 6|class QualityScorer:\\n 7| \\\"\\\"\\\"Score music videos on multiple dimensions\\\"\\\"\\\"\\n 8| \\n 9| def __init__(self):\\n 10| self.metrics = {}\\n 11| \\n 12| def score_audio(self, audio_path):\\n 13| \\\"\\\"\\\"Score audio quality (0-100)\\\"\\\"\\\"\\n 14| # TODO: Implement with librosa\\n 15| # - Dynamic range\\n 16| # - Clarity (spectral centroid)\\n 17| # - Stereo width\\n 18| # - Beat strength\\n 19| return {\\n 20| \\\"clarity\\\": 75.0,\\n 21| \\\"dynamics\\\": 70.0,\\n 22| \\\"stereo\\\": 80.0,\\n 23| \\\"beat_strength\\\": 72.0,\\n 24| \\\"overall_audio\\\": 74.25\\n 25| }\\n 26| \\n 27| def score_visual(self, video_path):\\n 28| \\\"\\\"\\\"Score visual quality (0-100)\\\"\\\"\\\"\\n 29| # TODO: Implement with opencv\\n 30| # - Sharpness (Laplacian variance)\\n 31| # - Color vibrancy\\n 32| # - Motion smoothness\\n 33| # - Composition\\n 34| return {\\n 35| \\\"sharpness\\\": 68.0,\\n 36| \\\"color\\\": 82.0,\\n 37| \\\"motion\\\": 71.0,\\n 38| \\\"composition\\\": 75.0,\\n 39| \\\"overall_visual\\\": 74.0\\n 40| }\\n 41| \\n 42| def score_sync(self, audio_path, video_path):\\n 43| \\\"\\\"\\\"Score audio-visual synchronization (0-100)\\\"\\\"\\\"\\n 44| # TODO: Beat detection vs visual peaks\\n 45| return {\\\"sync_accuracy\\\": 78.0}\\n 46| \\n 47| def score_overall(self, audio_path, video_path):\\n 48| \\\"\\\"\\\"Calculate overall quality score\\\"\\\"\\\"\\n 49| audio = self.score_audio(audio_path)\\n 50| visual = self.score_visual(video_path)\\n 51| sync = self.score_sync(audio_path, video_path)\\n 52| \\n 53| # Weighted average\\n 54| overall = (\\n 55| audio[\\\"overall_audio\\\"] * 0.4 +\\n 56| visual[\\\"overall_visual\\\"] * 0.4 +\\n 57| sync[\\\"sync_accuracy\\\"] * 0.2\\n 58| )\\n 59| \\n 60| return {\\n 61| \\\"audio\\\": audio,\\n 62| \\\"visual\\\": visual,\\n 63| \\\"sync\\\": sync,\\n 64| \\\"overall\\\": round(overall, 2)\\n 65| }\\n 66|\", \"total_lines\": 65, \"file_size\": 1910, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "tool_iuAMG1R2hfST3fCjroKUiNOW"
},
{
"role": "tool",
"content": "{\"content\": \" 1|#!/usr/bin/env python3\\n 2|\\\"\\\"\\\"Deliver videos to Alexander via Telegram\\\"\\\"\\\"\\n 3|import requests\\n 4|import os\\n 5|from pathlib import Path\\n 6|\\n 7|class VideoDeliveryBot:\\n 8| def __init__(self, token=*** chat_id=None):\\n 9| self.token=*** or os.environ.get(\\\"TELEGRAM_BOT_TOKEN\\\", \\\"8547467220:***\\\")\\n 10| self.chat_id = chat_id or \\\"7635059073\\\" # Alexander's ID\\n 11| self.api_url = f\\\"https://api.telegram.org/bot{self.token}\\\"\\n 12| \\n 13| def send_video(self, video_path, caption=\\\"\\\", reply_markup=None):\\n 14| \\\"\\\"\\\"Send video to Alexander\\\"\\\"\\\"\\n 15| url = f\\\"{self.api_url}/sendVideo\\\"\\n 16| \\n 17| with open(video_path, 'rb') as f:\\n 18| files = {'video': f}\\n 19| data = {\\n 20| 'chat_id': self.chat_id,\\n 21| 'caption': caption[:1024]\\n 22| }\\n 23| if reply_markup:\\n 24| data['reply_markup'] = reply_markup\\n 25| \\n 26| r = requests.post(url, files=files, data=data, timeout=60)\\n 27| return r.json()\\n 28| \\n 29| def send_daily_batch(self, videos_with_scores):\\n 30| \\\"\\\"\\\"Send top videos + runners up\\\"\\\"\\\"\\n 31| # Sort by score\\n 32| sorted_videos = sorted(videos_with_scores, key=lambda x: x['score'], reverse=True)\\n 33| \\n 34| top_3 = sorted_videos[:3]\\n 35| runners_up = sorted_videos[3:8]\\n 36| \\n 37| # Send header\\n 38| self.send_message(\\\"🎬 **Daily Music Videos** 🎬\\\\n\\\\nTop 3 + Runners Up\\\")\\n 39| \\n 40| # Send top 3\\n 41| for i, video in enumerate(top_3, 1):\\n 42| caption = f\\\"#{i} | Score: {video['score']}/100\\\\nStyle: {video.get('style', 'unknown')}\\\\n\\\\nReply with rating 1-10\\\"\\n 43| self.send_video(video['path'], caption)\\n 44| \\n 45| # Send runners up as list\\n 46| if runners_up:\\n 47| runner_text = \\\"**Runners Up:**\\\\n\\\" + \\\"\\\\n\\\".join([\\n 48| f\\\"{v['path'].name}: {v['score']}/100\\\" for v in runners_up\\n 49| ])\\n 50| self.send_message(runner_text)\\n 51| \\n 52| def send_message(self, text):\\n 53| \\\"\\\"\\\"Send text message\\\"\\\"\\\"\\n 54| url = f\\\"{self.api_url}/sendMessage\\\"\\n 55| r = requests.post(url, json={\\n 56| 'chat_id': self.chat_id,\\n 57| 'text': text,\\n 58| 'parse_mode': 'Markdown'\\n 59| }, timeout=10)\\n 60| return r.json()\\n 61|\", \"total_lines\": 60, \"file_size\": 2226, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "tool_2hc7KvUfh9Xfvm16oZT2vHuC"
},
{
"role": "assistant",
"content": "## Music Video Pipeline - Status Report\n\n**Pipeline executed: April 2, 2026 02:01 UTC**\n**Log location:** `/tmp/music_pipeline.log`\n\n### Summary\n\nThe pipeline ran but **failed during delivery** because the generation modules are stubs that don't actually produce video files.\n\n### What Happened\n\n| Stage | Status | Details |\n|-------|--------|---------|\n| Batch generation | ⚠️ **Mock only** | 5 video slots \"generated\" (epic orchestral, lo-fi, EDM, ambient, rock) |\n| Music generation | ⚠️ **Placeholder** | `music_gen.py` prints \"Would generate\" but creates no audio files |\n| Visual generation | ⚠️ **Placeholder** | `visual_gen.py` prints \"Would generate 60 frames\" but creates no frames |\n| Scoring | ⚠️ **Hardcoded** | Returns fake scores (74.25/100) without analyzing actual content |\n| **Delivery** | ❌ **FAILED** | `FileNotFoundError` - tried to send non-existent video files |\n\n### Root Cause\n\nThe pipeline is a **prototype skeleton** - the generation modules contain only placeholder implementations:\n\n```python\n# music_gen.py line 20\nprint(f\"[MusicGen] Would generate: {prompt} ({style})\")\nreturn str(output_path) # Path never created\n```\n\n```python\n# visual_gen.py line 15\nprint(f\"[VisualGen] Would generate {num_frames} frames: {prompt}\")\nreturn [] # No frames generated\n```\n\n### To Make This Functional\n\nThe pipeline needs:\n1. **Actual music generation** - Suno API integration or local MusicGen/Riffusion\n2. **Actual visual generation** - Stable Diffusion video (AnimateDiff, SVD) or Luma Dream Machine API\n3. **Video assembly** - FFmpeg to mux audio + frames into MP4\n4. **Real scoring** - librosa for audio analysis, opencv for visual quality\n\n### Recommendation\n\nThis pipeline needs implementation work before it can run overnight autonomously. Currently it's a well-structured framework waiting for the core generation engines to be plugged in.",
"reasoning": "Now I understand the full picture:\n\n1. The music video pipeline is a prototype/stub implementation\n2. It generates \"placeholder\" videos - just printing what it would do, not actually creating any files\n3. The delivery step tries to send files that don't exist, causing a FileNotFoundError\n4. The scoring module returns hardcoded fake scores\n\nThis is essentially a skeleton framework that was never fully implemented with actual video/music generation capabilities.\n\nI should report this to Alexander so he's aware the pipeline needs actual implementation.",
"finish_reason": "stop"
}
]
}
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