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Found 1,211 Skills
Use when "CrewAI", "multi-agent systems", "agent orchestration", "AI crews", or asking about "autonomous agents", "agent collaboration", "role-based agents", "agent workflows", "AI team coordination"
AI 도입 전략, Build vs Buy, 우선순위 설정, 거버넌스/보안, 6개월 확장 로드맵을 다루는 모듈.
module1~6 학습 내용을 복습하고 개념 연결성, 적용 판단력, 실행 계획까지 종합 점검하는 마무리 스킬.
Give agents persistent structural memory of a codebase — navigate dependencies, track public APIs, and understand why connections exist without re-reading the whole repo.
Explain and document MTHDS bundles. Use when user says "what does this pipeline do?", "explain this workflow", "explain this method", "walk me through this .mthds file", "describe the flow", "document this pipeline", "how does this work?", or wants to understand an existing MTHDS method bundle.
Orders scheduler. Reads .noodle/mise.json, writes .noodle/orders-next.json. Schedules work orders based on backlog state, plan phases, session history, and task type schedules.
A validation framework that ensures Claude's responses are current, accurate, complete, and clear. Use this skill whenever the user asks a factual or research question, requests analysis or recommendations (e.g., "What's the best framework for X?", "Compare options for Y"), or any prompt where recency and accuracy matter. Also trigger when the user explicitly asks for validated, verified, or fact-checked answers. This skill should activate broadly — if the answer depends on facts that could have changed in the last few months, use it. Even questions that seem straightforward ("Is X still the recommended approach?") benefit from this skill's validation pipeline. Do NOT trigger for purely creative writing, casual chat, or tasks that are entirely opinion-based with no factual claims.
Use when the user needs prompt design, optimization, few-shot examples, chain-of-thought patterns, structured output, evaluation metrics, or prompt versioning. Triggers: new prompt creation, prompt optimization, few-shot example design, structured output specification, A/B testing prompts, evaluation framework setup.
Guide for building high-quality MCP (Model Context Protocol) servers in Python or Node/TypeScript to integrate external APIs/services.
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested. Integrated into Cavekit: enabled by default for build, inspect, and subagent phases via caveman_mode config. See scripts/bp-config.sh for caveman_mode and caveman_phases.
Run the corpus benchmark — booster locally, optional Gemini/Sonnet/Opus baselines — and persist a verifiable measured-vs-claimed table
Build autonomous self-evolving AI agents with vision-grounded memory that operate computers through a perceive-reason-act cycle