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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s-skills
by s0613S-skills 하네스. 프로젝트 상태를 감지하고 docs-organize, test-scenario 스킬을 오케스트레이션한다. /s-skills 하나로 지금 무엇이 필요한지 판단해 적절한 스킬을 호출.
sj-outsource
by s0613외주 연결 스킬. 사용자가 배포·구현에 막히면 프로젝트 개요와 대화 맥락을 로컬 리포트로 정리하고, 사용자의 기본 메일 앱을 열어 전문가(SongSeungJu, farchicken00@naver.com)에게 보낼 초안을 채운다. 전송 버튼은 사용자가 누른다.
sj-agent-dev
by s0613비즈니스 에이전트 개발 전문가. 실제 회사 업무에 투입할 AI 에이전트를 설계·구현할 때 사용한다. 런타임 루프, 오케스트레이션, 역할 분리, 도구 계층화, 컨텍스트 관리, 가드레일, 옵저버빌리티, 메모리 계층, 평가·자기반성, 그래프 토폴로지의 10가지 축을 기준으로 구체적인 아키텍처 설계와 코드 구현을 안내한다. 에이전트 아키텍처 설계, 비즈니스 자동화 에이전트 구현, 멀티에이전트 오케스트레이션, 에이전트 운영 안전성 강화가 필요할 때 이 스킬을 활성화한다.
sj-pm
by s0613PM 역할 에이전트. 태스크를 분석하고 요구사항, 리스크, 우선순위, 역할 힌트를 정의한다. 결과는 .state/pm-brief.md(휘발)에, 학습 인사이트는 pm-context.md(영속)에 누적한다. /office-hours 모드: 코딩 전 6개 강제 질문으로 아이디어를 검증한다.
sj-qa
by s0613QA 역할 에이전트. pm-brief(요구사항 원본)과 실제 변경 파일을 직접 탐색해 독립 검증한다. dev-summary.md(구현자 자기 평가) 참조 금지 — Judge 독립성 원칙. PASS / FAIL / CONDITIONAL 판정을 .state/qa-verdict.md에 저장하고 PROJECT.md를 갱신한다. qa-context.md에 학습된 검증 포인트를 누적한다. /canary: 배포 후 프로덕션 상태 모니터링. /benchmark: Core Web Vitals + 로드 시간 기준 측정.
sj-cso
by s0613CSO(Chief Security Officer) 역할 보안 감사 에이전트. OWASP Top 10 + STRIDE 위협 모델링을 체계적으로 수행한다. "보안 점검", "취약점 검사", "보안 감사", "OWASP", "보안 리뷰" 요청에 반응.
sj-retro
by s0613주간 엔지니어링 회고 에이전트. 프로젝트별 배송 지표·테스트 건강도·성장 기회를 분석한다. "회고", "retro", "이번 주 정리", "retrospective", "지난주 리뷰" 요청에 반응.
sj-secretary
by s0613프로젝트 상태 보고 에이전트. 모든 프로젝트의 PROJECT.md를 읽어 어떤 프로젝트가 어떤 작업 중이고, 목표까지 현재 어떤 단계이며, 다음 할 일이 무엇인지 우선순위로 정렬 출력. 보고서 에코 없음. WBS/KPI 없음. 어디서 시작할지 한눈에.
sj-seo
by s0613Google Search Console + Naver Search Advisor 색인 자동화 전문가. "색인 등록", "검색에 안 나와", "Search Console 등록", "네이버 색인", "sitemap 제출", "검색 노출 도와줘" 요청을 받아 브라우저를 직접 열고 끝까지 자동 처리한다.
sj-ship
by s0613릴리즈 엔지니어 자동화 에이전트. 테스트 → 커버리지 감사 → PR 오픈까지 한 번에. "배포해줘", "PR 올려줘", "릴리즈", "ship", "머지해줘" 요청에 반응.
sj-spec
by s0613스펙 작성 전문가. 모호한 의도를 5단계(why·scope·technical·draft·file)를 거쳐 실행 가능한 정밀 스펙으로 변환한다. "스펙 만들어줘", "요구사항 정리", "PRD 써줘", "기능 명세" 요청에 반응.
sj-tech-lead
by s0613Tech Lead 역할. .state/pm-brief.md를 받아 필요한 전문 개발 서브에이전트 (frontend/backend/database/devops/security/data/si)를 식별·병렬 디스패치하고, 기술 리뷰·Security cross-review·Design 시각 리뷰(sentinel)를 거쳐 .state/dev-summary.md로 집계한다. 결과는 PROJECT.md와 dev-context.md에 반영.
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.