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...
s-skill-work-log-scrap
by Salesmap-tech내가 뭐했는지 분석해주는 스킬. GitHub, Linear, Slack 데이터를 종합해서 활동 요약 리포트를 생성한다. Use when asked to "뭐했는지", "활동 분석", "activity", "what did I do", "오늘 뭐했지", "이번주 뭐했지", "작업 요약", "활동 요약", "work log", "log scrap".
s-skill-interview-to-ticket
by Salesmap-tech인터뷰 피드백에서 SAL(Engineering) 팀 티켓 초안을 작성할 때 사용. "인터뷰 티켓", "피드백 티켓", "SAL 티켓 만들어" 등의 요청 시 자동 호출.
s-skill-linkedin-scrap
by Salesmap-tech링크드인 포스트 스크랩 스킬. 키워드나 인물명으로 링크드인 포스트를 검색·수집·저장. Use when asked to "링크드인 스크랩", "linkedin scrap", "포스트 모아줘", "링크드인 검색", "linkedin search", or when the user wants to collect LinkedIn posts.
s-skill-setup
by Salesmap-techs-skills 설치 후 MCP 서버(Linear/Slack/Notion)와 GitHub CLI를 대화형으로 설정하는 스킬. 사용 중인 도구만 골라 설정하고, 이미 돼 있으면 스킵, 안 돼 있으면 단계별 안내·검증까지 전부 끌고 간다. Use when asked "셋업", "setup", "처음 설정", "s-skills 설치했어요", or after installing s-skills.
s-skill-shiftee
by Salesmap-tech시프티(Shiftee) 근태/휴가 조회 스킬. 첫 호출 시 shiftee CLI를 자동 다운로드하여 휴가 내역, 출퇴근 기록, 스케줄, 누락 조회, 출퇴근 수정 요청 등 근태 관련 질문에 답한다. Use when asked about vacation, attendance, leave, schedule, clock-in/out, 휴가, 출퇴근, 근태, 스케줄, 누락, or anything HR/time-tracking related.
s-skill-slack
by Salesmap-tech슬랙 조회/작성 스킬. Slack MCP를 사용해서 채널 메시지 검색, 스레드 읽기, 메시지 작성을 대신 해준다. Use when asked to "슬랙 찾아줘", "슬랙 보내줘", "slack 메시지", "슬랙 검색", "최근 슬랙", "슬랙 디엠", "slack search", or anything involving Slack reads/writes.
s-skill-ui-writing
by Salesmap-techUI 문구 작성 및 검토. 컴포넌트 유형과 상황을 전달하면 SMWS 기준으로 문구를 작성하거나 기존 문구를 검토한다. Use when asked to "UI 문구", "문구 작성", "문구 검토", "라이팅", "writing", "토스트 문구", "다이얼로그 문구", "에러 메시지", "버튼 텍스트", or anything involving UI copy writing/review.
s-skill-teardown
by Salesmap-techs-skills 정리 스킬. Slack 앱/토큰, MCP 설정(Slack/Linear/Notion), Shiftee 로그인, GitHub CLI 로그인, 설치된 스킬 파일 자체까지 하나씩 물어보면서 선택적으로 제거한다. setup의 반대편. Use when asked "셋업 지워", "정리", "teardown", "uninstall", "로그아웃", "토큰 폐기", "슬랙 봇 지워", "시프티 로그아웃", "스킬 지워", or after "이제 안 써".
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.