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...
external-pr-review
by postmelee외부 기여자 PR 검토 절차를 적용한다. 명시 호출 시에만 사용한다. PR 초기 triage 권장값(reviewer/assignee/label/milestone)을 먼저 안내하고, 작업지시자 수락 후 mydocs/pr/pr_{N}_review.md 작성, 검증, 권고 판단을 수행한다. 수정 요청/보류/닫기 또는 내부 후속 분리 필요 시 코멘트 초안은 응답으로만 제시한다. 승인된 GitHub 공개 review/comment는 body-file 검증 후 등록한다. 외부 기여자 PR 전용 (내부 타스크에는 사용 금지).
task-start
by postmelee하이퍼-워터폴 타스크 시작 절차를 적용한다. 명시 호출 시에만 사용한다. GitHub 이슈 등록 확인, 통합 브랜치 최신화, local/task{N} 브랜치 생성, 오늘할일 항목 추가, 수행계획서 템플릿 생성을 수행한다. 새 코드/문서 변경을 시작하기 전 진행 단계 정렬 용도.
task-stage-report
by postmelee하이퍼-워터폴 타스크의 단계 종료 절차를 적용한다. 명시 호출 시에만 사용한다. 단계별 완료 보고서(`_stage{N}.md`) 작성, 단계 소스와 보고서 묶음 커밋, 단계 검증 명령 실행을 수행한다. 한 단계가 끝나고 다음 단계 진입 직전에 호출.
task-register
by postmelee하이퍼-워터폴 작업에서 아직 GitHub Issue가 없는 신규 타스크를 등록한다. 명시 호출 시에만 사용한다. 열린 milestone과 기존 label을 조회해 후보를 고르고, 이슈 생성 전 작업지시자 확인을 받은 뒤 GitHub Issue 번호를 만든다. 이슈 생성 후 브랜치/오늘할일/수행계획서는 task-start 절차로 넘긴다.
task-final-report
by postmelee하이퍼-워터폴 타스크의 최종 보고와 PR 게시 절차를 적용한다. 명시 호출 시에만 사용한다. 최종 결과 보고서(`_report.md`) 작성, 오늘할일 완료 처리, 최종 커밋, publish/task{N} 원격 push, 통합 브랜치 대상 Open PR 생성을 수행한다. 모든 단계 완료 후 PR 직전에만 호출.
pr-merge-cleanup
by postmeleePR merge 확인 후 부산물을 정리하는 절차를 적용한다. 명시 호출 시에만 사용한다. GitHub 이슈 close, publish/task{N} 원격 브랜치 삭제, 로컬 local/task{N} 브랜치와 분리 worktree 정리, 대상 통합 브랜치 복귀를 수행한다. 외부 PR 운영 기록 커밋이 대상 통합 브랜치에 로컬로만 남아 있으면 직접 push 여부를 확인한다. PR이 실제로 merge된 직후에만 호출.
external-pr-complete
by postmelee외부 기여자 PR이 merge 또는 cherry-pick 반영된 뒤 완료 처리를 수행한다. 명시 호출 시에만 사용한다. PR/Issue 완료 코멘트 초안 작성, pr_{N}_report.md 작성, 승인 후 GitHub 코멘트 등록, 관련 Issue close, mydocs/pr/archives 이동을 처리한다. 첫 기여자 환영, 구체적 기여 칭찬, maintainer 후속 보완 안내를 완료 메시지에 반영한다. 외부 PR 운영 기록만 바뀐 경우 별도 PR 없이 대상 통합 브랜치에 직접 커밋/푸시한다. external-pr-review 이후 merge/반영이 끝난 외부 PR 전용.
task-final-report
by postmelee应用 Hyper-Waterfall task 的 final report 和 PR publication 流程。 编写 final report（`_report.md`），将 daily task board 标记为完成， 创建 final commit，push 远程 publish/task{N} branch，并创建面向 {BASE_BRANCH} 的 Open PR。 只在所有 Stage 完成后、PR publication 之前立即调用。
task-register
by postmeleeRegister a new task that does not yet have a GitHub Issue in the Hyper-Waterfall workflow. Query open milestones and existing labels, choose candidates, confirm with the task requester before creating the Issue, then create the GitHub Issue number. After Issue creation, hand off branch, daily task board, and task plan work to task-start.
task-stage-report
by postmeleeApply the stage completion procedure for a Hyper-Waterfall task. Write the stage report (`_stage{N}.md`), commit stage source and report together, and run stage verification commands. Invoke after a stage completes and before entering the next stage.
task-start
by postmeleeApply the Hyper-Waterfall task start procedure. Confirm the GitHub Issue, update {BASE_BRANCH}, create local/task{N}, add a daily task board row, and create the task plan template. Use before starting new code or documentation changes.
todo
by postmeleeCreate and update the Hyper-Waterfall daily task board (`mydocs/orders/yyyymmdd.md`). Apply milestone table format, status update rules, and backlog section rules. When task-start, task-stage-report, task-final-report, or pr-merge-cleanup updates the daily task board, it follows this SKILL's format.
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.