av

Answer questions about how 'mi' works, write new tools, or modify the harness. Use for "how do you work", "write a tool", "add a tool", "create a tool", "extend yourself", "edit yourself", "what tools do you have", or any introspection/modification of the running agent.

Scan the codebase and classify every fact by lifecycle stage — tag @draft, @spec, or @implemented based on what the code actually shows. Add missing facts, fix inaccurate ones, remove obsolete ones. Use when asked to discover facts, bootstrap or update a fact sheet, scan the codebase for truths, sync facts to match the code, or audit the fact sheet for accuracy.

Operate on @spec facts — implement them in code, then tag @implemented. Use when asked to implement facts, implement the spec, build from the fact sheet, make facts true, or work through unimplemented requirements.

Operate on @draft facts — collaboratively refine them into precise, actionable @spec facts. Resolve ambiguities, fill gaps, eliminate contradictions, and sharpen labels until every fact is ready to implement. Use when asked to refine facts, clarify the spec, review facts for quality, or "work on facts" with the user.

Manage .facts files — atomic, validatable truth statements about a project. Install, check, list, add, edit, remove, and lint facts via the CLI. ALWAYS read this skill when the user mentions facts in any capacity.

Write a new SKILL.md to teach yourself a procedure for a recurring task. Use when asked to "write a skill", "create a skill", "add a skill", "remember how to X", "make a procedure for X", or when you notice a task pattern worth recalling in future sessions.

Answer "how does X work", "where is X defined", or "trace through Y" questions about a codebase using parallel subagent searches with cited summaries.

Run parallel or isolated subagents for independent subtasks (research, parallel analysis, one-shot transforms) to avoid bloating the main context.

Orchestrate AI coding agents (Claude Code, Codex, OpenCode) in isolated sandboxes using the @ai-hero/sandcastle SDK. Use when the user needs to (1) run agents AFK in Docker/Podman containers, (2) build multi-agent pipelines with plan-execute-review patterns, (3) run parallel agents on separate worktrees, (4) create iterative agent loops with maxIterations, (5) extract structured output from agent runs, (6) set up sandcastle in a new or existing project, or (7) write prompt files with template args and shell expressions.

Configure and operate the Claude Code harness for large codebases. Builds CLAUDE.md hierarchies, scoped test/lint commands, file exclusions, codebase maps, hooks, skills, subagent strategies, and LSP/MCP wiring. Use when setting up Claude Code for a new repo, auditing an existing configuration, onboarding a team, or scaling from single-developer to org-wide deployment. Triggers on "set up Claude Code for this repo", "optimize my Claude Code config", "audit my CLAUDE.md", "make this codebase navigable", "configure hooks/skills/plugins".

Bulletproof agent operating protocol. 15 failure-prevention rules distilled from 120+ real sessions and 10 agent definitions. Covers fabrication, constraint tracking, verification, scoping, retry discipline, and communication. Load before any task to prevent the most common agent failure modes.

Timeboxed ideation on a topic using propose-and-critique subagent pairs. Use when the user wants to brainstorm, explore ideas, discover features, generate options, or think through possibilities for a specified duration. Triggers on requests like "brainstorm X for 30 minutes", "ideate on X", "spend an hour thinking about X", "what features should we build", "explore options for X".

Comprehensive guide for setting up and running local LLMs using Harbor. Use when user wants to run LLMs locally, set up or troubleshoot Ollama, Open WebUI, llama.cpp, vLLM, SearXNG, Open Terminal, or similar local AI services. Covers full setup from Docker prerequisites through running models, per-service configuration, VRAM optimization, GPU troubleshooting, web search integration, code execution, profiles, tunnels, and advanced features. Includes decision trees for autonomous agent workflows and step-by-step troubleshooting playbooks.

Install, configure, and work with Turso DB — an in-process SQLite-compatible relational database engine written in Rust. Use when the user needs to (1) install Turso DB, (2) create or query databases with the tursodb CLI shell, (3) use Turso from JavaScript/Node.js via @tursodatabase/database, (4) work with vector search or embeddings in Turso, (5) set up full-text search with FTS indexes, (6) configure transactions including MVCC concurrent transactions, (7) enable encryption at rest, or (8) use Change Data Capture (CDC) for audit logging.

Systematically explore and test any software project (CLI, API, Backend, Library, etc.) to find bugs, usability issues, and edge cases. Produces a structured report with full reproduction evidence (exact commands, inputs, logs, and tracebacks) for every issue.

Use when the user requests integration testing, feature validation, or test plan execution

Perform Harbor release procedures — version bumping, codegen, committing, pushing, and drafting GitHub releases. Use this skill when the user wants to release a new version of Harbor, bump the version number, create a release on GitHub, run the release codegen pipeline, or anything related to shipping a new Harbor version. Triggers on phrases like "release Harbor", "bump version", "new release", "ship a new version", or "prepare a release".

Add a new service to Harbor — scaffold the compose config, environment variables, metadata, documentation, and cross-service integrations. Use this skill whenever the user wants to add a new service to Harbor, integrate a new tool/app/model server, create a compose configuration for a new project, or onboard any software into the Harbor ecosystem. Triggers on phrases like "add X to Harbor", "new service", "integrate Y", "onboard Z", "create a service for", or when the user provides a GitHub repo link and expects it to become a Harbor service. Even if the user just says a project name and implies they want it in Harbor, this skill applies.

Comprehensive guide for setting up and running local LLMs using Harbor. Use when user wants to run LLMs locally, set up or troubleshoot Ollama, Open WebUI, llama.cpp, vLLM, SearXNG, Open Terminal, or similar local AI services. Covers full setup from Docker prerequisites through running models, per-service configuration, VRAM optimization, GPU troubleshooting, web search integration, code execution, profiles, tunnels, and advanced features. Includes decision trees for autonomous agent workflows and step-by-step troubleshooting playbooks.

Create new Harbor Boost modules — the Python plugins that run inside Harbor's LLM proxy. Use this skill whenever the user wants to build a Boost module, write a custom module for Harbor Boost, add a new feature to the Boost proxy pipeline, or create any kind of middleware that transforms, augments, or intercepts LLM chat completions in Harbor. Also triggers when the user mentions "boost module", "boost plugin", "custom module for boost", or wants to add prompt engineering, reasoning chains, or output transforms to Harbor's proxy layer.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Manage .facts files — atomic, validatable truth statements about a project. Install, check, list, add, edit, remove, and lint facts via the CLI. ALWAYS read this skill when the user mentions facts in any capacity.

Scan the codebase and classify every fact by lifecycle stage — tag @draft, @spec, or @implemented based on what the code actually shows. Add missing facts, fix inaccurate ones, remove obsolete ones. Use when asked to discover facts, bootstrap or update a fact sheet, scan the codebase for truths, sync facts to match the code, or audit the fact sheet for accuracy.

Operate on @spec facts — implement them in code, then tag @implemented. Use when asked to implement facts, implement the spec, build from the fact sheet, make facts true, or work through unimplemented requirements.

Operate on @draft facts — collaboratively refine them into precise, actionable @spec facts. Resolve ambiguities, fill gaps, eliminate contradictions, and sharpen labels until every fact is ready to implement. Use when asked to refine facts, clarify the spec, review facts for quality, or "work on facts" with the user.

Scan the codebase and classify every fact by lifecycle stage — tag @draft, @spec, or @implemented based on what the code actually shows. Add missing facts, fix inaccurate ones, remove obsolete ones. Use when asked to discover facts, bootstrap or update a fact sheet, scan the codebase for truths, sync facts to match the code, or audit the fact sheet for accuracy.

Operate on @spec facts — implement them in code, then tag @implemented. Use when asked to implement facts, implement the spec, build from the fact sheet, make facts true, or work through unimplemented requirements.

Operate on @draft facts — collaboratively refine them into precise, actionable @spec facts. Resolve ambiguities, fill gaps, eliminate contradictions, and sharpen labels until every fact is ready to implement. Use when asked to refine facts, clarify the spec, review facts for quality, or "work on facts" with the user.

Manage .facts files — atomic, validatable truth statements about a project. Install, check, list, add, edit, remove, and lint facts via the CLI. ALWAYS read this skill when the user mentions facts in any capacity.

Install and run the pace personal dashboard. Covers cloning, dependency install via Bun, Docker and Docker Compose deployment, CLI flags (--config, --port), environment variable overrides (PACE_CONFIG, PORT), themed starter configs, and troubleshooting common startup errors. Use when asked to set up, install, deploy, or run a pace dashboard.

Generate a config.yaml for the pace personal dashboard from a user's natural-language description of interests. Maps topics to content adapters (RSS, Hacker News, Reddit, GitHub, arXiv, YouTube, Mastodon, etc.), composes transform pipelines, designs flexbox layouts, and optionally wires up LLM-powered summarization and ranking. Use when asked to configure, set up feeds for, or customize a pace dashboard.

Systematically explore and test any software project (CLI, API, Backend, Library, etc.) to find bugs, usability issues, and edge cases. Produces a structured report with full reproduction evidence (exact commands, inputs, logs, and tracebacks) for every issue.

Release remotion-bits to GitHub, Cloudflare, and npm. Use when bumping the package version, updating CHANGELOG.md, building the registry, creating the release commit, pushing master, opening a prefilled GitHub release form, deploying docs, and publishing to npm.

Animation components and utilities for Remotion video projects. Use when building Remotion compositions with text animations, gradient transitions, particle effects, 3D scenes, or staggered motion effects. Provides example bits (complete compositions) and reusable components that can be installed via jsrepo.