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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youtube-collector
by greatSumini유튜브 채널을 등록하고 새 컨텐츠를 수집하여 자막 기반 요약을 생성하는 skill. 사용자가 (1) 유튜브 채널 등록/관리를 요청하거나, (2) 등록된 채널의 새 영상 수집을 요청하거나, (3) 유튜브 영상 요약을 요청할 때 사용. 데이터는 .reference/ 폴더에 YAML 형식으로 저장됨.
transcribe
by greatSumini로컬 오디오 파일(m4a 등)을 한국어로 전사하는 skill. "전사해줘", "받아쓰기", "음성 파일 텍스트로", "transcribe", "음성 받아적어줘", "녹음 파일 변환" 등 오디오 → 텍스트 변환 요청에 트리거된다.
email-draft
by greatSumini비즈니스 메일/답장 초안을 작성할 때 적용하는 글쓰기 매너. "초안 작성", "메일 작성", "답변 작성", "회신", "메일 써줘", "draft email", "reply" 등 메일 관련 작성 요청에 트리거된다.
discord-message
by greatSumini사용자에게 Discord DM을 전송한다. 메세지 내용을 입력받아 봇을 통해 개인 메세지를 보낸다.
gmail
by greatSuminigogcli를 사용하여 Gmail 메일을 검색, 조회, 발송하는 skill. "메일 검색해줘", "이메일 보내줘", "gmail search", "메일 확인해줘", "최근 메일", "메일 답장", "gmail send", "메일 보내줘", "안 읽은 메일" 등 Gmail 관련 요청에 트리거된다.
cc-usage-audit
by greatSumini한 프로젝트에서 사용자가 Claude Code에 입력한 프롬프트·작업 이력을 분석해 (1) 사용 패턴 정량화, (2) 사용자의 개발 철학 추출(근거 인용), (3) 그 철학을 렌즈로 한 메타 시스템(하니스·게이트·CI·프로세스) audit, (4) 우선순위가 매겨진 개선점 발굴을 수행한다. 트리거 — "내 프롬프트 분석해줘", "claude code 사용 패턴 분석", "내 개발 철학 추출", "메타 시스템 점검/audit", "하니스 개선점 발굴", "내가 입력한 프롬프트 기반으로 개선점 파악" 및 유사 의도.
google-calendar
by greatSuminigogcli를 사용하여 Google Calendar 일정을 조회, 검색, 생성, 수정, 삭제하는 skill. "일정 확인해줘", "캘린더 보여줘", "오늘 일정", "이번주 일정", "미팅 잡아줘", "일정 추가", "calendar events", "일정 생성", "스케줄 확인", "빈 시간 확인", "일정 삭제" 등 Google Calendar 관련 요청에 트리거된다.
hook-creator
by greatSuminiCreate and configure Claude Code hooks for customizing agent behavior. Use when the user wants to (1) create a new hook, (2) configure automatic formatting, logging, or notifications, (3) add file protection or custom permissions, (4) set up pre/post tool execution actions, or (5) asks about hook events like PreToolUse, PostToolUse, Notification, etc.
ship
by greatSumini지금까지의 작업을 한 번에 출하한다 — 커밋 → push → PR 생성 → squash merge. "commit push pr squash merge", "ship it", "출하해줘", "PR 만들고 머지해줘", "커밋푸시 PR 스쿼시", "변경사항 commit push 후 pr 생성하고 squash merge" 등 commit+push+PR+merge를 한 번에 끝내려는 의도에서 트리거된다. 커밋만/푸시만 원하면 commit skill을 쓴다.
slash-command-creator
by greatSuminiGuide for creating Claude Code slash commands. Use when the user wants to create a new slash command, update an existing slash command, or asks about slash command syntax, frontmatter options, or best practices.
subagent-creator
by greatSuminiCreate specialized Claude Code sub-agents with custom system prompts and tool configurations. Use when users ask to create a new sub-agent, custom agent, specialized assistant, or want to configure task-specific AI workflows for Claude Code.
harness-starter
by greatSuminiA portable orchestrator that completes a high-level task through 5 stages — clarify → context-gather → plan → implement → verify. Each stage hands off via file artifacts, and a stage is skipped when its artifact already exists. The plan is decomposed into a task graph (DAG); independent nodes run in parallel, dependent nodes run in order.
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.