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...
naver-apartment-listings
by JiHeeP네이버 부동산에서 특정 아파트명, 면적(전용/공급), 거래유형(매매/전세/월세), 가격 조건을 받아 해당 조건의 호가 목록을 수집/정리한다. Use when user asks to fetch current Naver Real Estate listings for a specific apartment with filters.
cbi-inquiry-3tier
by JiHeePCoach the user to generate 3-tier inquiry questions (factual, conceptual, debatable) that build a thinking path toward an approved micro generalization. Use only after generalization gate passed.
cbi-orchestrator
by JiHeePOrchestrate Concept-Based Inquiry (CBI) coaching end-to-end. Use when the user wants the full 1) curriculum random matching -> 2) generalization coaching with strict gate -> 3) 3-tier inquiry questions flow. Do NOT use for single-step requests.
edu-deep-talk
by JiHeeP교육학 텍스트를 읽고 정리한 내용을 바탕으로, 단계별 심화 대화(티키타카)를 통해 수업 적용까지 연결하는 스킬. 교육과정 성취기준 데이터를 활용하여 이론과 실제 수업을 구체적으로 연결한다. 요약 파일(.md) 또는 자유 텍스트 입력 모두 지원한다. 대화 종료 후 '대화 요약'과 '수업 설계안' 두 가지 결과물을 생성한다. 이 스킬은 사용자가 "심화 대화", "디프톡", "수업에 어떻게 적용", "이걸로 수업하려면", "깊이 파보자", "더 생각해보자" 등의 표현을 쓸 때 트리거한다.
edu-text-summarizer
by JiHeeP교육학 텍스트(책, 논문, 자료)를 일관된 구조화 틀로 요약하여 Markdown 파일로 생성하는 스킬. 사용자가 읽은 내용을 텍스트로 전달하면, 핵심 개념 정리 · 수업 적용 아이디어 · 개인 메모를 포함한 구조화된 요약 문서를 만든다. 한 권의 책을 장(chapter)별로 반복 요약하는 패턴을 지원한다. 이 스킬은 사용자가 "요약해줘", "정리해줘", "이 장 읽었는데", "교육학 책", "독서 정리", "수업 적용", "개념 정리" 등의 표현을 쓸 때 트리거한다. 교육학뿐 아니라 교직 관련 텍스트를 읽고 정리하려는 모든 상황에서 적극적으로 사용한다.
real-estate-news-briefing
by JiHeePCreate daily Korean real-estate news briefings with source priority (MOLIT first, then Maeil Business and Hankyung), topic filtering (supply/presale, rates/loans, policy/regulation), duplicate removal, impact analysis, and day-over-day comparison. Use when user asks for daily real-estate news summary/report/briefing automation.
thinking-muscle-trainer
by JiHeeP사고 근육 훈련 스킬. 실제 같은 관계/일상 상황을 랜덤 생성하고, 사용자가 6단계 사고 프레임워크(팩트↔해석 분리 → 감정 이름 붙이기 → 숨은 가정 찾기 → 기준 명시 → 반대 시나리오 → 행동 결론)를 연습하도록 단계별로 안내한 뒤, AI가 사용자의 사고 과정을 검증하고 잘한 점 3개·보완할 점 3개를 피드백한다. 이 스킬은 사용자가 "사고 연습", "사고 근육", "판단 연습", "감정 분리 연습", "상황 연습", "시나리오 훈련", "thinking muscle" 등을 언급할 때 트리거한다. 관계, 직장, 육아, 교직, 일상 대인관계에서의 판단력 훈련 요청에도 적극적으로 사용한다.
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.