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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docx
by 7loroUse this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
flutter-expert
by 7loroFlutter/Dart 코드 작성, 리뷰, 리팩토링을 위한 전문가 스킬. Flutter 공식 AI rules 기반으로 위젯 설계, 상태 관리, 테마, 라우팅, 테스트, 아키텍처 가이드를 제공한다. Riverpod 상태 관리 전문 지식 포함: AsyncNotifierProvider, 코드 생성, Repository 패턴, Family Provider, autoDispose, 성능 최적화, 테스트 패턴 등 2025 모범 사례를 제공한다. 트리거: Flutter/Dart 코드 작성, 위젯 구현, 상태 관리 설계, 테마/스타일링, 라우팅 설정, Flutter 테스트 작성, Flutter 프로젝트 구조 설계, pubspec.yaml 수정, "flutter", "dart", "위젯", "widget", "riverpod", "provider", "AsyncNotifier" 등의 키워드가 포함된 코드 작업 시.
git-master
by 7loroGit 전문가 스킬. Atomic commit 분할, rebase/squash, history 검색(blame, bisect, log -S)을 수행. 트리거: "git-master", "/git-master", "atomic commit", "커밋 분할", "커밋 나눠", "rebase", "리베이스", "squash", "스쿼시", "히스토리 정리", "history cleanup", "git blame", "bisect", "누가 바꿨어", "언제 바뀌었", "find when", "who changed", "커밋 정리", "변경사항 정리해서 커밋". git-master는 다수 파일을 atomic 단위로 분할 커밋하고 rebase/history 검색까지 수행하는 종합 Git 전문가.
gmail-improved
by 7loroGmail API를 통해 다중 Google 계정의 이메일을 읽고, 검색하고, 보내고, 관리하는 스킬. 트리거: "이메일 확인", "메일 읽어줘", "메일 보내줘", "이메일 검색", "check email", "send email", "reply to email", "search inbox" 등.
google-calendar-improved
by 7loroGoogle Calendar API를 통해 다중 계정의 일정을 조회, 생성, 수정, 삭제하는 스킬. 병렬 SubAgent로 여러 계정 동시 조회, 충돌 감지, 시간대 자동 처리 지원. 트리거: "일정 확인", "캘린더", "오늘 일정", "이번 주 일정", "일정 잡아줘", "미팅 만들어줘", "일정 변경", "일정 삭제", "스케줄" 등.
handoff
by 7loroHANDOFF.md를 작성/업데이트하여 다음 에이전트가 컨텍스트 없이도 작업을 이어받을 수 있게 한다. git 히스토리, 코드베이스, 현재 대화를 분석하여 목표·진행 상황·성공/실패 경험·다음 단계를 정리한다. 트리거 — "handoff", "인수인계", "HANDOFF.md", "핸드오프", "프로젝트 정리", "현재 상태 정리", "다음 세션을 위해 정리", "작업 내용 정리해줘" 등. HANDOFF.md 업데이트 요청 시에도 사용한다.
language-tutor
by 7loro사용자의 언어 학습을 돕는 튜터 스킬. 초등학생도 이해할 수 있는 쉬운 설명과 비유로 기초부터 점진적으로 학습을 확장한다. 영어, 일본어를 지원한다. 트리거: 언어 학습 관련 요청 시 (문법, 어휘, 회화, 발음, 번역 설명 등). "영어 문법 알려줘", "일본어 동사 변형", "영어 발음", "일본어 배우고 싶어" 등의 요청에 사용한다.
make-backlog-comment-jp
by 7loroBacklog 이슈에 PR 주소와 변경사항 요약을 일본어+한국어 병기 코멘트로 남기는 스킬. 일본어 비즈니스 문체(ます체)로 3줄 요약을 먼저 작성하고, 구분선 아래에 한국어 요약을 병기한다. 트리거 - "backlog 코멘트 일본어", "백로그 코멘트 jp", "일본어 코멘트", "JP 코멘트", "make-backlog-comment-jp" 등. 일본인 팀원이 있는 프로젝트에서 PR 작업 내용을 Backlog에 기록할 때, 또는 사용자가 일본어 병기를 명시적으로 요청할 때 사용한다. 한국어만 필요하면 make-backlog-comment 스킬을 대신 사용한다.
make-backlog-comment
by 7loroBacklog 이슈에 PR 주소와 변경사항 요약 코멘트를 한국어로 남기는 스킬. 현재 브랜치의 PR 정보를 분석하여 3줄 요약을 자동 생성하고 Backlog API로 코멘트를 등록한다. 트리거 - "backlog 코멘트", "백로그 코멘트", "코멘트 남겨", "PR 코멘트 backlog", "이슈에 코멘트", "make-backlog-comment" 등. PR을 올린 후 Backlog 이슈에 작업 내용을 기록하고 싶을 때, 또는 이슈 키를 지정하여 코멘트를 남기고 싶을 때 사용한다. 일본어 병기가 필요하면 make-backlog-comment-jp 스킬을 대신 사용한다.
make-pr
by 7loro변경사항 커밋, 브랜치 푸시, 드래프트 PR 생성을 자동화하는 워크플로우. 트리거: "PR 만들어", "PR 생성해줘", "PR 생성", "풀리퀘스트 생성", "PR 올려", "make pr", "/make-pr" 등. Stacked PR 지원 및 PR 템플릿 자동 생성.
pkm
by 7loroObsidian PKM vault 관리 스킬. 노트 생성/편집/검색, Daily Journal 관리, PR 문서화, 책/영화 노트 생성 지원. 트리거: "pkm" 키워드 포함 시, "노트 작성/추가/편집" 요청 시, "저널에 기록" 요청 시, PR URL/번호 언급 + 문서화 요청 시, "vault에서 찾아줘" 검색 요청 시, "책 추가", "영화 추가", "book", "movie", "읽은 책", "본 영화" 등 도서/영화 노트 생성 요청 시.
pptx
by 7loroUse this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.
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.