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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xss-lucy-jsoup
by puk0806Spring Boot XSS 방어 패턴 - Naver Lucy XSS Servlet Filter(요청 파라미터 레벨) + jsoup Safelist(HTML 본문 sanitize) 조합. Spring Boot 2.5 / 3.x 양쪽, FilterRegistrationBean 등록, rule XML 설정, CSP 헤더, 한국 엔터프라이즈 실무 패턴
aristotelian-virtue-ethics-detail
by puk0806도덕윤리교육 대학원생(석/박사)이 akrasia(NE VII) 학위논문 또는 KCI 등재지 1차 텍스트 챕터 깊이를 더하기 위해 NE I–VI권(eudaimonia·덕·중용·자발성·선택·정의·실천적 지혜)과 VIII–X권(우정·즐거움·관조)을 Bekker 행 번호 단위로 정리한 상세 가이드. 각 권을 akrasia 논의와 연결하는 매핑 표 포함. <example>사용자: "II권에서 hexis와 ethismos가 정확히 어디 나오지? akrasia 극복 논의에 연결하려면?"</example> <example>사용자: "III권 prohairesis 논의 범위가 어디까지야? akrates의 선택 분석에 인용하려고."</example> <example>사용자: "VI권 phronēsis 정의 위치가 1140a24-b30 맞아? VII권 1146a4-9와 어떻게 연결돼?"</example>
dissertation-defense-prep
by puk0806대학원생이 박사·석사 학위논문 본심사·디펜스 직전에 발표 자료, 질의응답, 실무 행정, 심리 준비를 체계화하기 위한 스킬. 한국 주요 대학원 가이드와 국제 디펜스 표준(미국 PhD defense, 영국 viva voce)을 종합한다. <example>사용자: "다음 달 박사 본심사인데 발표자료 30매 어떻게 구성하지?"</example> <example>사용자: "디펜스에서 모르는 질문이 나오면 어떻게 답해야 해?"</example> <example>사용자: "디펜스 전날 챙겨야 할 체크리스트 만들어줘"</example>
mui-v5
by puk0806MUI v5 + Emotion 핵심 패턴 — ThemeProvider, sx prop, styled(), 컴포넌트 오버라이드, TypeScript 테마 확장, 반응형 레이아웃, 성능 최적화, Next.js App Router 통합
web-speech-api-stt
by puk0806브라우저 내장 Web Speech API의 SpeechRecognition(STT) 사용 패턴. window.SpeechRecognition || window.webkitSpeechRecognition prefix 처리, lang/continuous/interimResults/maxAlternatives 옵션, onresult event.resultIndex로 interim+final 분리 처리, transcript/confidence 추출, 마이크 권한(getUserMedia) 사전 확인, Chrome·Safari 지원/ Firefox·Edge 미지원 graceful degradation, iOS Safari 백그라운드 중단·연속 인식 timeout, abort() vs stop() 차이, 한국어(ko-KR) 인식 팁, React useSpeechRecognition 훅 패턴
naver-seo-specifics
by puk0806네이버 통합검색 알고리즘(C-Rank, D.I.A., D.I.A.+, 스마트블록)과 한국 검색 시장 특수성. 자체 도메인을 한국 시장에 노출하려는 운영자가 알아야 할 네이버 SEO만의 메커니즘과 Google과의 차이 정리.
korean-dream-interpretation-tradition
by puk0806한국 전통 꿈 해몽을 민속학적·문헌학적 자료로 정리한 스킬. 『동의보감』 내경편, 『삼국유사』, 한국민족문화대백과사전, 국립민속박물관 한국민속신앙사전 등 1차/공인 학술 출처만 사용한다. 비과학적 한계를 본문 박스로 명시하며, 앱·서비스 적용 시 hedging 톤을 강제한다. <example>사용자: "한국 전통에서 돼지꿈은 어떻게 해석돼?"</example> <example>사용자: "동의보감은 꿈을 어떻게 분류해?"</example> <example>사용자: "꿈 해몽 앱 만드는데 톤 가이드 줘"</example>
n8n-workflow-design
by puk0806n8n 워크플로우 설계 패턴 — 노드 구조, 데이터 흐름, 재사용, 표현식, 디버깅, 모범 사례. n8n.io의 노코드·로우코드 자동화 플랫폼에서 유지보수 가능한 워크플로우를 짜는 방법을 정리한다.
jackson-time-migration
by puk0806Jackson + 자바 시간 API 통합 — Joda-Time(레거시)에서 java.time(JSR-310)으로의 마이그레이션, Spring Boot Jackson 시간 직렬화 설정, LocalDateTime/OffsetDateTime/ZonedDateTime/Instant 선택 기준, 타임존 처리, MyBatis 연동
aristotle-primary-citation
by puk0806도덕윤리교육 전공 대학원생(석/박사)이 아리스토텔레스(특히 akrasia 주제) 1차 텍스트를 인용할 때 Bekker 번호, 작품 표준 약어, 영역본/그리스어 비평본/국역본 표기를 정확히 사용하도록 돕는 스킬. <example>사용자: "akrasia 논의 시작 부분을 NE 표기로 어떻게 인용하지?"</example> <example>사용자: "강상진 공역본을 각주에 어떻게 적어야 해?"</example> <example>사용자: "Bekker 번호 1147a24-b5 형식이 맞나?"</example>
web-vitals-rum-comparison
by puk0806RUM(Real User Monitoring)으로 실사용자 Core Web Vitals(LCP·INP·CLS)를 수집하고 Sentry·Datadog에서 배포(release) 전후 p75를 비교하는 패턴을 정리한 스킬. Lab data와 RUM의 차이, INP 측정 시점·SPA hidden state·표본 분포 함정까지 포함. <example>사용자: "배포 후 LCP가 나빠졌는지 어떻게 비교하지?"</example> <example>사용자: "Sentry RUM에서 v1.2.0과 v1.2.1의 INP p75 차이를 보고 싶어"</example> <example>사용자: "Datadog RUM Explorer에서 release별 Core Web Vitals 쿼리 어떻게 짜?"</example>
moral-curriculum-2022-achievement-standards
by puk08062022 개정 도덕과 교육과정(교육부 고시 제2022-33호 [별책 6]) 성취기준 전문(全文) 부록. 도덕윤리교육 대학원생이 학위논문에서 성취기준을 정확히 인용할 때 NCIC PDF를 매번 열지 않고 참조할 수 있도록 정리. <example>사용자: "akrasia 논문 3장에 4도01-XX 성취기준을 인용해야 하는데 정확한 전문 좀 알려줘"</example> <example>사용자: "9도01-01 전문이랑 영역명이랑 정확한 학년군 표기 알려줘. 인용 형식도"</example> <example>사용자: "2015 개정 4도01-01과 2022 개정 4도01-01이 같은 코드인데 내용이 다르지? 차이 정리해줘"</example>
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.