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...
v4-showcase
by daniel-kim-9wayShowcase 등록 + 졸업. Triggers: "v4 showcase", "v4-showcase", "쇼케이스", "졸업"
deck-builder
by daniel-kim-9way발표자료(PPTX)를 디자인 시스템 기반으로 생성한다. 사용자가 폰트와 색/스타일을 주면 그 테마로, 안 주면 기본(크림·코랄) 테마로 만든다. 제안서·보고서·강의·가이드북 등 덱 유형별 구성을 지원한다. 파라미터화된 레이아웃 라이브러리로 수정 가능한 .pptx를 만든다. "PPT/발표자료/강의안/제안서/보고서/가이드북 만들어줘", "이 스타일로 슬라이드", "우리 브랜드 색으로 덱" 등에 사용.
v1-bigpicture-check
by daniel-kim-9wayCh.1의 마무리 Reflection — 1-1~1-5에서 배운 5가지 부품(브라우저·도메인·서버·DB·외부서비스)을 본인 SaaS 한 장 지도로 통합. 자가진단 체크리스트로 Ch.2 진입 준비도를 확인합니다. Triggers: "v1 bigpicture", "v1-bigpicture-check", "인프라 지도", "Ch.1 정리", "자가진단"
v1-data-flow
by daniel-kim-9way가입 버튼 클릭 → 입력값 → DB 표 → 비밀번호 해시 → 로그인까지 7단계 시뮬레이션. DB 표는 엑셀 시트와 같다는 비유, 비밀번호 평문 저장 금지, OAuth 위임 원리. Triggers: "v1 data-flow", "v1-data-flow", "데이터 흐름", "DB 흐름", "비밀번호 해시", "OAuth"
v1-director
by daniel-kim-9way코딩 환경 3가지(에디터·터미널·git)를 감독 비유로 친숙하게 익히는 스킬. Ch.4 BUILD 전에 도구 이름과 역할을 머리에 그려두는 것이 목적. 실제 설치는 Ch.4에서 진행. Triggers: "v1-director", "코딩 환경", "에디터", "터미널", "git", "감독"
v1-discover
by daniel-kim-9wayClaude AI 도구 발견 + 비용 시뮬레이터. claude.ai / Claude Desktop / Claude Code / API 4가지 차이를 정리하고, 예산·학습 속도 기반 구독 플랜을 추천하고, API 비용 알람을 설정하도록 안내한다. Triggers: "v1-discover", "AI 도구 추천", "Claude 플랜", "비용 시뮬레이터"
v1-firstbuild
by daniel-kim-9wayAI와 30분 만에 자기소개 페이지 만들기. 이름·소개·잘하는 것·디자인 톤을 대화로 받아 HTML 파일 1개를 생성하고, 선택 시 Netlify Drop으로 인터넷 배포까지 안내한다. Triggers: "v1-firstbuild", "첫 빌드", "자기소개 페이지", "first build"
v1-money-flow
by daniel-kim-9way결제(PG) 흐름을 메인 사례로, 외부 API 호출(Claude API/카톡)이 모두 같은 패턴 — "외부 서비스에 일 시키고 결과 받기" — 임을 보여줍니다. API key는 .env, webhook은 외부가 우리에게 거는 전화. Triggers: "v1 money-flow", "v1-money-flow", "결제 흐름", "PG", "API 호출", "webhook", "외부 서비스"
v1-webflow
by daniel-kim-9way0-3 자기소개 페이지를 base로 사용자 가입 기능을 붙이는 시뮬레이션을 통해 웹 서비스의 큰 그림(브라우저·도메인·서버·DB·배포)을 매핑합니다. Triggers: "v1 webflow", "v1-webflow", "웹 서비스 큰 그림", "인프라 매핑", "웹플로우"
v2-service-type
by daniel-kim-9way내 서비스 유형 결정하기. C2 PLAN 두 번째 스킬. Triggers: "v2 service-type", "v2-service-type", "서비스 유형", "유형 결정"
v3-auth
by daniel-kim-9way홈페이지 + 회원가입/로그인. C3 BUILD 일곱 번째 스킬. Triggers: "v3 auth", "v3-auth", "회원가입", "로그인"
v3-checkpoint
by daniel-kim-9way체크포인트 - 내 서비스 동작 확인 + GitHub. C3 BUILD 열 번째 스킬. Triggers: "v3 checkpoint", "v3-checkpoint", "C3 체크포인트", "서비스 점검"
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.