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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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harness
by revfactory하네스를 구성합니다. 전문 에이전트를 정의하며, 해당 에이전트가 사용할 스킬을 생성하는 메타 스킬. (1) '하네스 구성해줘', '하네스 구축해줘' 요청 시, (2) '하네스 설계', '하네스 엔지니어링' 요청 시, (3) 새로운 도메인/프로젝트에 대한 하네스 기반 자동화 체계를 구축할 때, (4) 하네스 구성을 재구성하거나 확장할 때, (5) '하네스 점검', '하네스 감사', '하네스 현황', '에이전트/스킬 동기화' 등 기존 하네스 운영/유지보수 요청 시 사용.
hashtag-science
by revfactory해시태그분석가(hashtag-analyst)가 사용하는 해시태그 과학 전문 스킬. 해시태그 피라미드 전략, 경쟁도 분석, 트렌드 예측, 플랫폼별 최적 해시태그 전략을 제공한다. '해시태그', '키워드 전략', '트렌드 분석', '해시태그 리서치' 등에 활용한다.
hashtag-science
by revfactoryA specialized skill for the hashtag-analyst agent covering hashtag science. Provides hashtag pyramid strategy, competition analysis, trend prediction, and platform-specific optimal hashtag strategies. Use for 'hashtags,' 'keyword strategy,' 'trend analysis,' 'hashtag research,' and similar topics.
hiring-pipeline
by revfactory채용 프로세스의 JD작성, 소싱, 스크리닝, 면접 설계, 평가, 오퍼까지 에이전트 팀이 협업하여 한 번에 생성하는 풀 파이프라인. '채용', '인재 채용', 'JD 작성', '직무기술서', '채용공고', '면접 질문', '면접 설계', '채용 프로세스', '소싱 전략', '스크리닝', '오퍼레터', '채용 파이프라인', 'hiring', '인재 확보' 등 채용 전반에 이 스킬을 사용한다. 단, 실제 채용 플랫폼(ATS) 연동, 급여 시스템 등록, 채용 계약서의 법적 효력 보장, 레퍼런스 체크 실행은 이 스킬의 범위가 아니다.
hook-writing
by revfactoryYouTube video hook (opening) writing skill. Provides 15 hook patterns, viewer retention psychology, and click-to-watch conversion formulas. Referenced by the scriptwriter agent when designing video openings to minimize viewer drop-off. Also used standalone for requests like 'show me hook patterns' or 'how to write great openings.' Full script writing and SEO optimization fall under the youtube-production skill.
hook-writing
by revfactoryYouTube 영상 훅(도입부) 작성 전문 스킬. 15가지 훅 패턴, 시청 유지율 심리학, 클릭-시청 전환 공식을 제공한다. 대본작가(scriptwriter)가 영상 도입부를 설계할 때 이 스킬을 참조하여 시청자 이탈을 최소화하는 훅을 작성한다. 단독으로 '훅 패턴 알려줘', '도입부 잘 쓰는 법' 요청에도 사용한다. 전체 대본 작성이나 SEO 최적화는 youtube-production 스킬의 영역이다.
hiring-pipeline
by revfactoryhiring process JDwriting, sourcing, screening, interview design, assessment, offerto agent team to Korean creation Full pipeline. 'hiring', 'talent hiring', 'JD writing', 'job description', 'hiringposting', 'interview question', 'interview design', 'hiring process', 'sourcing strategy', 'screening', 'offer', 'hiring pipeline', 'hiring', 'talent secure' etc. hiring before skill usage. However, actual hiring platform(ATS) annual, grade whensystem etc.record, hiring totalapprox.from legal capability report, reference execution is outside this skill's scope.
normalization-patterns
by revfactory데이터베이스 정규화/비정규화 패턴 라이브러리. 1NF~BCNF 판별 기준, 함수 종속성 분석, 정규화 단계별 변환 절차, 전략적 비정규화 패턴, 공통 도메인 ERD 템플릿을 제공하는 data-modeler 확장 스킬. '정규화', '비정규화', 'ERD 패턴', '함수 종속성', '테이블 분리', '관계 설계' 등 데이터 모델링 시 사용한다. 단, DDL 생성이나 쿼리 최적화는 이 스킬의 범위가 아니다.
narrative-structure-patterns
by revfactory비주얼 스토리텔링의 내러티브 구조 패턴 라이브러리. 3막/5막/영웅여정 구조, 감정 곡선 설계, 장면 전환 기법, 텍스트-이미지 밸런스 공식을 제공하는 story-architect 확장 스킬. '내러티브 구조', '스토리 아크', '감정 곡선', '장면 전환', '이야기 구조', '비주얼 리듬' 등 스토리 설계 시 사용한다. 단, 텍스트 집필이나 이미지 생성은 이 스킬의 범위가 아니다.
nlp-preprocessing-toolkit
by revfactory텍스트 전처리 기법 카탈로그: 토큰화, 정규화, 불용어, 형태소 분석, 임베딩 선택, 한국어 특화 처리 가이드. '텍스트 전처리', '토큰화', '형태소 분석', 'KoNLPy', '불용어', '정규화', 'TF-IDF', '임베딩', 'Word2Vec', '한국어 NLP' 등 텍스트 전처리 시 이 스킬을 사용한다. preprocessor와 extractor의 텍스트 처리 역량을 강화한다. 단, 감성분석 모델이나 분류 알고리즘 선택은 이 스킬의 범위가 아니다.
query-optimization-patterns
by revfactorySQL/NoSQL 쿼리 최적화 패턴, 실행 계획 분석, 인덱스 전략, N+1 문제 해결 등 데이터베이스 성능 최적화 가이드. '쿼리 최적화', '실행 계획', 'EXPLAIN', '인덱스 설계', 'N+1 문제', '느린 쿼리', 'slow query', 'DB 성능' 등 데이터베이스 쿼리 성능 개선 시 이 스킬을 사용한다. bottleneck-analyst와 optimization-engineer의 DB 성능 분석 역량을 강화한다. 단, 전체 시스템 프로파일링이나 벤치마크 실행은 이 스킬의 범위가 아니다.
quest-design-patterns
by revfactory퀘스트디자이너(quest-designer)가 사용하는 퀘스트 설계 패턴 전문 스킬. 퀘스트 아키타입, 보상 심리학, 난이도 곡선, 플레이어 동기 부여 프레임워크를 제공한다. '퀘스트 설계', '보상 체계', '미션 구조', '퀘스트 패턴' 등에 활용한다.
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.