Audit your Claude Code skills directory, CLAUDE.md, and recent session patterns to surface candidates for new skills -- gaps, redundancies, and formalization opportunities -- and to catch skill hygiene failures: runtime artifacts (venv, __pycache__, node_modules) living inside a skill directory, and absolute paths or identity leaking out of tracked files.
Fetch curated, versioned API documentation through the `chub` CLI before writing code against an external API or SDK, so calls are checked against real docs instead of recalled from memory. Use before implementing anything that calls an external API, when unsure of the correct parameters, methods, or patterns, when a model name or endpoint needs verifying, or when the user asks how to use a specific service's API.
Scan a codebase for LLM model usage and recommend tier, task-fit, and cost optimizations. Works on any project using Anthropic, Google, OpenAI, or open-source models. Use to check whether each task is running on the right model tier, when a new model generation ships and you want to see if you can tier down, when you suspect you are overspending on tokens for simple tasks, or for a periodic model hygiene review.
Evaluate an agent codebase against 12 infrastructure primitives (permission model, token budget, crash recovery, tool assembly, streaming events, state machine, provenance, stop reasons, boot sequence, verification harness, memory decay, health checks) and return a severity-ranked gap analysis with prioritized upgrade path. Use when auditing agent architecture, reviewing agent readiness, or planning what infrastructure to build next.
Build a personal multi-agent AI command center using Claude Code as the immutable foundation with independently swappable specialized layers — Agent SDK bridge, voice interface (Gemini Live + Pipecat), cross-agent hive mind, self-managing memory with decay/promotion, and a mission control dashboard. Use when designing a personal AI stack, replacing an agent framework, building a multi-agent system, or asking "how do I build my own agent platform".
Generate or lint an AGENTS.md file from repository analysis. Discovers build commands, test commands, environment variables, architecture patterns, and coding conventions, then produces a comprehensive AGENTS.md that tells AI coding agents how to work in the repo. Use when the user says "generate agents.md", "create agents.md", "lint agents.md", "agents file", "agent instructions", or wants to create standardized agent onboarding docs for a repository.
Scores any agent definition or skill manifest against the four consumer-AI breakthrough problems (context, reliability, permission, judgment) plus a reactive-vs-anticipatory axis. Outputs a heatmap and ranked vulnerability list.
Score an agent system design against the Bitter Lesson principle — how much "how" is encoded vs "what", how much bets on model improvement vs locks in current limitations. Flags procedural lock-in, hardcoded orchestration, and domain hacks. Produces a simplification roadmap. Use when designing new agent systems, reviewing agent architecture, or deciding what to simplify. Trigger on "bitter lesson", "score architecture", "agent complexity audit", "simplification roadmap", "how vs what ratio", "are we fighting the model".