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...
hwpx
by jkf87HWP/HWPX 문서(.hwp, .hwpx) 생성·변환·읽기·편집 통합 스킬. 'HWP 변환', 'hwp를 hwpx로', '한글 문서', 'hwpx', 'HWPX', '한글파일', '.hwpx 만들어줘', '보고서', '공문', '기안문', '한글로 작성', '회의록', '제안서', '이미지 포함 문서' 등의 키워드 시 반드시 사용. HWP→HWPX 변환, 마크다운·텍스트·URL→HWPX 변환, 템플릿 치환 워크플로우를 지원한다.
ohmyclaw
by jkf87OpenClaw 용 멀티프로바이더/멀티계정 라우팅 하네스. Z.ai 코딩플랜(Lite/Pro/Max) 모델 매트릭스 + ChatGPT Codex OAuth 다중 계정 풀(round-robin/cooldown/fan-out) + 추론 인식 모델 선택 + Plan→Work→Review 오케스트레이션 + 5관점 리뷰와 갭 감지. Use when: (1) Z.ai GLM 코딩 작업, (2) 다중 ChatGPT/Z.ai 계정으로 rate limit 분산, (3) 한국어 코딩/리뷰/리팩토링, (4) Plan→Work→Review 사이클로 다단계 작업 분해, (5) 같은 태스크를 여러 계정에 fan-out. NOT for: (a) 단순 1-line 수정, (b) read-only 탐색, (c) Z.ai/Codex 가 아닌 다른 프로바이더 단독 작업.
harness
by jkf87OpenClaw 하네스 — Plan→Work→Review 에이전트 오케스트레이션 + 모델 라우팅 + 채널 브릿지. Claude Code 하네스 생태계 분석 기반. GLM/GPT/Claude 모델 지원. GLM-5.1 포함. 한국어 감지→GLM 자동 라우팅. sessions_spawn으로 에이전트별 모델 별도 지정. 브릿지로 실시간 채널 알림.
rtk-setup
by jkf87RTK(Rust Token Killer)를 OpenClaw 워크스페이스에 설치하고, 모든 에이전트가 RTK 명령어를 강제로 사용하도록 AGENTS.md에 지침을 삽입하는 스킬. 사용 시점: (1) "RTK 설치해줘", (2) "토큰 절약", (3) "RTK 설정", (4) 새 워크스페이스 초기 세팅 시.
ppt-to-video-workflow
by jkf87PPT/슬라이드를 나레이션과 자막이 포함된 영상으로 변환합니다. PPTX 파일 또는 slides.json에서 슬라이드 이미지를 추출/렌더링하고, TTS로 나레이션을 생성하며, 자막을 추가하여 최종 MP4 영상을 만듭니다. "PPT를 영상으로 만들어줘", "발표 영상 생성", "자막 포함 영상 만들기" 요청 시 사용합니다.
remotion-shorts
by jkf879:16 세로형 숏폼 기술 해설 영상을 Remotion으로 제작. 노트/URL/텍스트/SRT를 다크 테마 타이포 모션그래픽 영상으로 변환하고, 에이전트 팀(기획/제작/검수) 기반으로 GIF+보이스오버+차트+이미지를 포함한 MP4를 렌더링. 사용 시점 - (1) 영상 만들어줘, 숏폼 만들어줘 요청 시, (2) 노트나 문서를 영상으로 변환 요청 시, (3) Remotion 기반 세로형 영상 제작 시, (4) shorts, 숏폼, 릴스, 세로 영상 키워드 시.
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.