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...
gemma
by ujuc로컬 LM Studio와 Google AI Studio(Gemini API)를 통해 Gemma 모델에 프롬프트를 전달한다. variant별 자동 라우팅(e2b/e4b→로컬, 26b/31b→원격)과 LM Studio 미가용 시 Gemini 폴백을 지원한다. gemma, gemma4, gemma로 요약해줘, gemma로 번역해, lm studio로 돌려줘, gemini api로 보내줘, 로컬 LLM, 오프라인 AI, 로컬로 처리해, 클라우드로 돌려줘, Gemma 호출 요청 시 사용한다. 민감 정보 오프라인 처리, 긴 컨텍스트 요약, 다국어 번역, 초안 생성 등에 적합.
agents
by ujucCreates and manages AGENTS.md files for AI agent integration. Use when the user asks to "에이전트해줘", "create agents", "AGENTS.md 만들어줘", "agents.md 업데이트", "agents 파일 검증", or needs to set up project guidance for AI agents.
interview
by ujucInterview user in-depth to create a detailed specification document. Use when the user asks to "interview me", "인터뷰해줘", "스펙 작성해줘", "요구사항 정리해줘", or needs to create a detailed spec through interactive Q&A.
annotate-plan
by ujuc병렬 에이전트로 구현 계획을 생성하고, 사용자 인라인 주석을 반복 처리하여 플랜을 개선한다. 구현 계획 작성, 플랜 만들어줘, annotate-plan, /annotate-plan, 노트 반영해줘, address notes, 주석 처리해, annotations 요청 시 사용한다.
generate-claude-md
by ujuc프로젝트용 CLAUDE.md, AGENTS.md, contributing-docs/, .claude/rules/ 파일을 발견 불가능 정보 원칙에 따라 생성하거나 업데이트한다.
generate-skills
by ujucClaude 스킬을 생성하거나 기존 스킬을 최신 spec에 맞게 업데이트한다. 스킬 만들어줘, 새 스킬 추가, 스킬 업데이트, 스킬 수정, generate-skills 요청 시 사용한다.
implement-plan
by ujuc주석이 달린 구현 플랜을 지속적 검증·블로커 감지·디버거 연동과 함께 실행한다. 순차/병렬(worktree) 실행 모드를 지원한다. 구현 시작, 플랜 실행해, implement-plan, 다 구현해, /implement-plan 요청 시 사용한다.
multi-agent-orchestrator
by ujucPlanner-Generator-Evaluator 3-agent 파이프라인으로 장시간 자율 코딩 세션을 오케스트레이션한다.
skill-index
by ujuc자체 스킬과 플러그인 명령을 8개 그룹과 워크플로우 색인으로 출력하는 메타스킬. 키워드를 잊었을 때 대화창에서 즉시 카탈로그를 본다.
spec-planner
by ujuc1-4문장 프롬프트를 상세 제품 스펙으로 확장한다. 범위는 야심차게, 구현 디테일은 Generator에게 위임한다.
commit
by ujucCreates git commits following team's version control guidelines. Use when the user asks to "commit changes", "create a commit", "make a commit", "커밋해줘", "변경사항 저장", "커밋 메시지 작성", "커밋 만들어줘", or needs to commit staged/unstaged changes to git.
refactor
by ujucSuggests and performs code refactoring following best practices. Use when user asks to "리팩토링 해줘", "refactor this", "코드 개선해줘", "정리해줘", "클린 코드로", "중복 제거해줘", "이거 더 깔끔하게", or wants to improve code quality without changing functionality.
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.