Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
fz-manage
by jaewooongyunfz 생태계 관리. 스킬 목록, 건강 체크, 벤치마크, 의존성 확인. 예: 스킬 목록 보여줘, 건강 체크해줘, 벤치마크 돌려줘
fz-memory
by jaewooongyun메모리 관리 + 교훈 회상. L1/L2 GC, topic file 관리. 예: 메모리 정리해줘, 교훈 떠올려줘, 기억 정리
fz-modernize
by jaewooongyun가이드/문서 모더나이제이션 — 외부 최신 자료(Tier 1+2)로 갱신 + stale 항목 정리 + 정량 검증 (Codex 3회 + AC8 link). 예: 최신화, 가이드 업데이트, 모더나이제이션, 문서 갱신, stale 정리
fz-new-file
by jaewooongyun새 소스 파일 생성 + 헤더 서명. 프로젝트 규칙에 맞는 파일 헤더 자동 생성. 예: 새 파일, 파일 생성, 헤더
fz-peer-review
by jaewooongyun팀원 PR 코드 리뷰. 3-Model Cross-Review + 9개 관점 독립 분석. 예: 팀원 PR 리뷰해줘, 피어리뷰, PR 검토
fz-plan
by jaewooongyun계획 수립 + 영향 범위 분석 + 설계. 요구사항 분해와 Serena 기반 코드베이스 탐색. 예: 계획 세워줘, 설계해줘, 아키텍처 잡아줘, 요구사항 분석
fz-pr-digest
by jaewooongyunPR 변경사항 해설 + 학습. Before/After 비교, 아키텍처 맥락 설명. 예: 설명해줘, 해설, 뭐가 바뀐, PR 이해
fz-pr
by jaewooongyunPR 생성. Fork 기반 푸시 + 티켓 연동 + 리뷰어 자동 지정. 예: PR 만들어, 풀리퀘스트, PR 생성
fz-recording
by jaewooongyun녹음 파일 → 회의록 변환. 화자 분리 + AI 첨언. 예: 녹음, 회의록, 화자 분리
fz-review
by jaewooongyun자기 코드 3중 검증(Claude+Codex+sc:analyze) + 역방향 검증. 예: 리뷰해줘, 검증해줘, 품질 확인, 괜찮아?, 내 코드 봐줘
fz-search
by jaewooongyun코드 탐색 + 의존성 추적. Serena 심볼 기반 정밀 검색. 예: 찾아줘, 탐색해줘, 어디 있어?, 누가 쓰고 있어?
fz
by jaewooongyun유니버셜 오케스트레이터. 자연어 → 스킬 파이프라인 자동 구성 + 복잡도 기반 팀 자동 편성 + 모델 승격. Use for ANY complex multi-step task that requires combining multiple skills, or when unsure which specific skill to use. Examples: "계획부터 PR까지", "버그 찾아서 고쳐줘", "리뷰하고 커밋해줘".
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.