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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haemilsia58
by temptation0924-design해밀시아58 검증 "Quiet Luxury" 스타일로 프리미엄 부동산/스튜디오/공간 대여 홈페이지를 제작하는 워크플로우. React 19 + Tailwind CSS 4 + shadcn/ui + framer-motion 기반. Cream+Deep Brown 2색 팔레트, Cormorant Garamond + Noto Sans KR 300, 10섹션 블루프린트 + Floating Call Button. 다음 키워드에서 반드시 이 스킬을 사용할 것 — "해밀시아58 스타일로", "haemilsia58 스타일", "해밀시아58처럼", "Quiet Luxury 홈페이지", "올드머니 스타일 홈페이지", "프리미엄 스튜디오 홈페이지", "프리미엄 펜션 홈페이지", "공간 대여 홈페이지", "촬영 스튜디오 홈페이지", "독채 소개 홈페이지". 프리미엄 공간 홈페이지 제작 요청 감지 시 자동 트리거.
naver-powerlink-advisor
by temptation0924-design네이버 파워링크 검색광고 전문 파트너 스킬. 신규 캠페인 세팅부터 일상 운영·진단·개선까지 끝까지 옆에 두고 쓰는 광고 운영 전문가. 캠페인 5종(파워링크/쇼핑검색/브랜드검색/플레이스/파워콘텐츠) · 광고그룹 분류 · 키워드 클러스터링·제외키워드 · 소재(제목 15자/설명 45자) · 확장소재 13종(전화/위치/예약/계산/추가제목/추가설명/홍보문구/서브링크/가격링크/이미지/이미지서브링크/플레이스/홍보영상/블로그리뷰) · 품질지수 7칸 · 자동/수동 입찰·가중치 · CTR/CVR/CPA/ROAS 진단 · 네이버 프리미엄 로그분석+GA4 UTM 전환 추적까지 50+ 요소 마스터. 다음 키워드에서 반드시 자동 트리거 — "파워링크", "네이버 광고", "네이버 검색광고", "광고 세팅", "광고 시작", "키워드 잡아줘", "키워드 클러스터링", "광고 카피", "광고 문안", "확장소재", "품질지수", "노출순위", "입찰가", "수동입찰", "자동입찰", "CTR 개선", "CVR 개선", "CPA", "ROAS", "광고 진단", "광고 점검", "리포트 진단", "캠페인 진단", "광고그룹 설계", "랜딩 매칭", "광고-랜딩", "전환 추적", "GA4 UTM", "프리미엄 로그분석", "주간 점검", "월간 점검", "광고 시작하려고", "파워링크 시작", "네이버 광고 첫", "광고 효율", "광고비 줄이고", "등록 안 돼", "검수 거절", "특수문자 허용", "소재 차단", "이미지형 서브링크", "이미지 사이즈", "1:1 정사각형", "비즈채널 연결", "위치정보 안 보임", "홍보문구 종류", "할인 이벤트 등록", "타겟별 USP". 캡처/스크린샷/CSV 리포트 붙여넣으면 자동 진단 모드 진입.
naver-search-advisor
by temptation0924-design🔎 네이버 서치어드바이저(웹마스터도구) 사이트 등록 전체 자동화. 소스코드에 robots.txt + sitemap.xml + naver-site-verification 메타 슬롯 추가 → 커밋·push → 배포 검증 → Playwright로 네이버 콘솔 접속 → QR 로그인 → 사이트 추가 → 소유확인 → 사이트맵 제출 → 웹페이지 수집 요청까지 원클릭. 다음 키워드에서 반드시 이 스킬을 사용할 것 — "네이버 서치어드바이저 등록", "네이버 웹마스터 등록", "네이버 SEO 등록", "searchadvisor 등록", "네이버 사이트맵 제출", "naver-site-verification", "네이버 크롤러 등록", "사이트 네이버 등록". 네이버 검색 노출/SEO 작업 요청 감지 시 자동 트리거.
japandi-interior-redesign
by temptation0924-designJapandi(Japanese + Scandinavian) 스타일 인테리어 리디자인 워크플로우. 사용자가 업로드한 방 사진을 기반으로 기존 공간의 구조(벽, 바닥, 빌트인 가구 등)를 유지하면서 가구와 소품을 Japandi 스타일로 변경하는 이미지 생성 작업을 수행. 다음 키워드에서 반드시 이 스킬을 사용할 것 — "Japandi 스타일로", "자판디 스타일로", "재팬디 스타일로", "방 사진 리디자인", "인테리어 재디자인", "방 꾸며줘", "미니멀 인테리어", "북유럽 인테리어", "일본 스칸디 인테리어", "방 사진 업로드", "내 방 이렇게 바꿔줘". 방 사진 + 스타일 변경 요청 감지 시 자동 트리거.
travel-meal-planner
by temptation0924-designUse when planning meals for a trip. Designs meal slots by time of day, checks menu compatibility, verifies restaurants via Kakao Map reviews (Playwright scraping), and outputs a final meal plan. Triggers on "여행 맛집", "식사 플랜", "맛집 찾아줘", "travel meal plan", "몇끼 먹을지".
danggeun-zone-search
by temptation0924-design🥕 당근마켓 동탄·수원 zone 일괄 검색 스킬. 대표님 위치(석우동) 기준 가까운 순으로 매물 자동 정렬. zone ID 63개 박제(동탄 22 + 영통구 19 + 팔달구 22) + 본체/액세서리 자동 분리 + 미개봉/S급 우선순위 + 중고나라·다나와 시세 비교 통합. 반드시 이 스킬을 사용할 것 — "당근 검색", "당근 중고", "당근 매물", "Z플립 매물", "아이폰 매물", "갤럭시 매물", "중고폰 찾아줘", "동탄 당근", "수원 당근", "석우동 매물", "당근 동탄~수원", "거리순 매물", "동탄 가까운 중고", "중고 가성비". 대표님이 당근에서 뭔가 사려고 할 때 자동 트리거.
haemilsia-d0-test
by temptation0924-designUse when planning marketing design deliverables — documents, landing pages, blog images, or any content that needs both message strategy and visual direction. Acts as a design planning partner that structures thinking before execution. Triggers: "디자인 기획", "마케팅 자료", "랜딩페이지 기획", "블로그 이미지 방향", "문서 디자인", "카피 잡아줘", "컨셉 잡아줘", "마케팅 디자인", "홍보물 기획", "세일즈 자료", "초안 봐줘", "브리프 만들어줘", "카드뉴스 기획", "제안서 기획", "SNS 콘텐츠", "디자이너한테 전달". Proactively suggest when user describes a marketing goal but hasn't structured the design approach yet.
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.