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.
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domain-research
by hongswUse when conducting systematic research in any domain (AI, healthcare, manufacturing, etc.), transforming vague interests into structured research through conversational discovery, or when users need evidence-based insights from broad exploration to actionable plans
pm-coach
by hongswPM 코치 - 업무 소통 최적화. 두서없는 업무 지시나 채팅 로그를 분석해 요청/수신/보고 모드로 구조화된 업무 문서를 생성한다. "정리해줘", "이거 어떻게 써야 해", "업무 지시 받았는데" 같은 요청에 사용.
agentic-learning
by hongswClaude Code 자기주도 학습 스킬. "/agentic-learning", "/learn", "학습", "스킬 배우기" 요청에 사용. ai-native-camp/camp-1 기반의 인터랙티브 학습 프레임워크. PPTX 자동 생성 프로젝트를 통일 예시로 사용.
korean-persona-search
by hongsw한국어 퍼소나 데이터셋(nvidia/Nemotron-Personas-Korea, 100만 행)에서 직무·지역·연령·학력 등 다축 조건으로 후보를 검색하고 다양성 샘플링으로 N개를 반환. 한국 페르소나/한국인 캐릭터/한국 시나리오 에이전트 정의에 근거가 필요하거나, '한국어 페르소나 찾아줘', '한국 직장인 페르소나', '특정 지역/연령대 페르소나'를 요청하면 반드시 이 스킬을 사용할 것. 데이터셋 다운로드·로컬 캐시·Parquet 필터·다양성 샘플링까지 일괄 처리한다. Also triggers on English requests: 'find/search Korean personas', 'sample from Nemotron-Personas-Korea', 'Korean persona candidates by job/age/region'.
korean-persona-harness
by hongsw한국어 퍼소나(Nemotron-Personas-Korea) 기반으로 임의 도메인의 에이전트 팀을 만들어주는 메타 하네스 오케스트레이터. 한국 업무·문화·언어 감각이 살아있는 에이전트가 필요할 때 사용한다. '한국어 페르소나로 팀 만들어줘', '한국인 캐릭터 에이전트 만들어줘', '한국 X 도메인 하네스 만들어줘', '한국 페르소나로 X 시나리오 에이전트 생성', '한국어 화법으로 다시 만들어줘', '페르소나 다시 뽑아서 팀 재구성', 후속 요청('업데이트', '재실행', '보완', '추가')에서도 트리거된다. 일반 harness와 달리 한국어·한국 문화 맥락에 특화. Also triggers on English requests: 'make/build a Korean persona team', 'create Korean character agents', 'Korean cultural agents for <domain>', 'rebuild team with Korean personas'.
korean-voice-adapter
by hongswNemotron-Personas-Korea 등에서 가져온 raw 한국어 퍼소나 카드를 에이전트 정의에 적합한 한국 직장 매너·존댓말 레벨·업종별 화법으로 가공한다. 한국어 에이전트 정의에 voice/tone 가이드가 필요하거나, '한국 페르소나에 말투/화법 입혀줘', '존댓말 레벨 결정해줘', '한국 업무 톤으로 다듬어줘'를 요청하면 이 스킬을 사용할 것. 호칭·존댓말·보고 매너·산업별 어휘를 결정한다. Also triggers on English requests: 'apply Korean voice/tone', 'add Korean honorifics', 'tone for Korean workplace', 'industry-specific Korean speech'.
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.