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...
korean-cardnews-generator
by qkrehgk1-wq주제나 키워드, 또는 긴 글을 받아 인스타그램·카카오채널용 한국어 카드뉴스 구성안을 만듭니다. 표지 1장 + 본문 5~7장 + 마무리 1장 구조로, 각 장의 헤드라인·본문·시각 디자인 가이드를 제공합니다. Triggers on "카드뉴스 만들어줘", "인스타 카드뉴스", "카드뉴스 구성", "SNS 콘텐츠 기획", "create Korean card news".
korean-email-writer
by qkrehgk1-wq상황과 요점을 받아 한국 비즈니스 맥락에 맞는 이메일을 작성합니다. 격식 수준(공식/일반/친근)과 상황(요청·사과·제안·거절·팔로업)에 맞춰 제목·본문·맺음말을 완성하고, 보내기 전 체크포인트도 제공합니다. Triggers on "이메일 써줘", "메일 작성", "비즈니스 이메일", "정중하게 메일", "거절 메일", "write Korean business email".
korean-meeting-notes
by qkrehgk1-wq회의 녹취록·메모·대화 기록을 받아 구조화된 한국어 회의록으로 정리합니다. 결정사항, 액션 아이템(담당자·기한 포함), 논의 요약, 미결 사항을 분리해 깔끔하게 출력합니다. Triggers on "회의록 정리", "회의 정리해줘", "미팅 노트", "결정사항 정리", "액션 아이템 뽑아줘", "organize Korean meeting notes".
korean-report-summarizer
by qkrehgk1-wq한국어 업무 보고서·문서를 받아 3줄 핵심 요약, 실행 가능한 액션 아이템 5개, 그리고 소셜미디어 공유용 한 줄 문장으로 변환합니다. 주간보고·회의자료·기획서·이메일 등 긴 한국어 문서를 빠르게 소화해야 할 때 사용하세요. Triggers on "보고서 요약", "이거 요약해줘", "핵심만 뽑아줘", "액션 아이템 정리", "summarize Korean report".
korean-writing-refiner
by qkrehgk1-wq거칠거나 어색한 한국어 글을 받아, 글쓴이의 뜻과 어조는 살리면서 더 자연스럽고 명확하게 다듬습니다. 맞춤법·띄어쓰기·번역투·군더더기·비문을 고치고, 무엇을 왜 바꿨는지 함께 알려줘 글쓰기 실력도 늘게 합니다. 필요하면 격식 수준(공식/일반/편안)도 조절합니다. Triggers on "윤문해줘", "문장 다듬어줘", "이 글 매끄럽게", "자연스럽게 고쳐줘", "한국어 교정", "어색한 부분 고쳐줘", "글 다듬기", "polish my Korean writing".
korean-code-review
by qkrehgk1-wq코드나 변경분(diff)을 받아 한국어로 친절한 코드 리뷰를 작성합니다. 버그·보안·성능·가독성 관점에서 문제를 짚고, 왜 문제인지와 개선 코드를 함께 제시합니다. 주니어도 이해할 수 있는 한국어 설명으로 팀 코드 리뷰 부담을 줄입니다. Triggers on "코드 리뷰", "이 코드 봐줘", "리뷰해줘", "코드 검토", "PR 리뷰", "review this code in Korean".
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.