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...
my-cash-position
by eoashCash Position 자동 업데이트. Plaid API로 Chase/Hanmi 은행 잔액·입출금 조회 + Google Sheets 자동 업데이트. "캐시", "cash", "잔액", "cash position" 요청에 사용.
my-finance-advisor
by eoashEO Studio 재무 어드바이저. Chris 페르소나 기반 재무 조언, AR 전략, 캐시플로우 분석, FX 판단. "재무 조언", "캐시 상황", "AR 분석", "FX 환전", "재무 리뷰", "/finance" 요청에 사용.
my-consult
by eoashEO Studio 어드바이저 자동 라우팅 메타 스킬. 입력 키워드를 감지해 Alex(코드), Lisa(법률), Chris(재무), Mia(디자인), Ryan(PM) 중 적합한 어드바이저에게 자동 라우팅. "상담해줘", "물어볼게", "/consult" 요청에 사용.
my-sync-us-revenue
by eoash미국 매출 동기화 (Bill.com 인보이스 + Plaid 입금 매칭 → Google Sheets). "미국 매출", "US revenue", "입금 확인", "sync revenue", "미입금" 요청에 사용.
hwpx
by eoash한글(HWPX) 문서 생성/읽기/편집 스킬. .hwpx 파일, 한글 문서, Hancom, OWPML 관련 요청 시 사용.
my-eo-contract-drafter
by eoashEO Studio 표준 계약서 docx 생성기. "계약서 써줘", "영상제작 계약서", "클린본 라이선스", "2차 활용 계약" 같은 요청을 받으면 서식 헬퍼 + 법인정보 레지스트리 + 템플릿을 조합해 Word에서 바로 날인 가능한 docx 초안을 생성한다. 7단계 플레이북 강제 + docx 서식 표준(굴림/바탕 10pt, 여백 상 3·하 2·좌우 2.5cm) 준수 + 내부 검토 메모 페이지 자동 부착.
my-content-digest
by eoash콘텐츠를 받으면 Quiz-First 방식으로 학습하는 스킬. 퀴즈부터 내고 틀린 부분을 같이 공부한다. "콘텐츠 소화", "퀴즈", "학습", "digest" 요청에 사용.
day1-onboarding
by eoashAI Native Camp Day 1 온보딩. Claude와 대화하면서 Claude Code를 익힌다. "1일차", "Day 1", "온보딩" 요청에 사용.
day2-create-context-sync-skill
by eoashAI Native Camp Day 2 Context Sync 스킬 만들기. 여러 외부 도구에서 컨텍스트를 수집하여 하나의 sync 문서로 만드는 나만의 스킬을 직접 구축한다. "2일차", "Day 2", "context sync", "컨텍스트 싱크", "sync 스킬", "스킬 만들기", "정보 수집 스킬" 요청에 사용.
day2-supplement-mcp
by eoashDay 2 보충 자료 - MCP 딥다이브. Claude Code에 외부 도구를 연결하는 MCP를 체계적으로 배운다. "MCP 배우기", "MCP 보충", "MCP 강의", "MCP 학습" 요청에 사용.
day4-wrap-and-analyze
by eoashAI Native Camp Day 4 Wrap & Analyze. session-wrap 스킬을 직접 만들고, history-insight와 session-analyzer로 세션을 분석한다. "4일차", "Day 4", "wrap", "세션 분석", "session wrap", "세션 래핑" 요청에 사용.
day5-fetch-and-digest
by eoashAI Native Camp Day 5 콘텐츠 소화 스킬 만들기. fetch-tweet, fetch-youtube, content-digest 3개 스킬을 직접 만들고 활용한다. "5일차", "Day 5", "fetch", "콘텐츠 스킬", "트윗 스킬", "유튜브 스킬", "다이제스트 스킬" 요청에 사용.
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.