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 Canine89한글(HWPX) 문서 생성/읽기/편집 스킬. .hwpx 파일, 한글 문서, Hancom, OWPML 관련 요청 시 사용.
hwpx
by Canine89HWPX 문서(.hwpx 파일)를 생성, 읽기, 편집, 템플릿 치환하는 스킬. '한글 문서', 'hwpx', 'HWPX', '한글파일', '.hwpx 파일 만들어줘', 'HWP 문서 생성', '보고서', '공문', '기안문', '한글로 작성' 등의 키워드가 나오면 반드시 이 스킬을 사용할 것. 한글과컴퓨터(한컴)의 HWPX 포맷(KS X 6101/OWPML 기반, ZIP+XML 구조)을 python-hwpx 라이브러리로 다룬다. 보고서 양식이 필요하면 assets/ 폴더의 레퍼런스 템플릿을 활용한다. 일반 Word(.docx) 문서에는 docx 스킬을 사용할 것.
remotion-shorts
by Canine89Remotion 기반 세로형(9:16) 쇼츠 슬라이드 영상 생성. YouTube Shorts, Instagram Reels, TikTok 등 세로 영상용. 사용자가 주제·내용을 제공하면 Paperlogy 폰트, 컬러 테마, 애니메이션이 적용된 쇼츠 슬라이드를 생성한다. "쇼츠 만들어줘", "shorts", "세로 슬라이드", "릴스" 등의 요청에 사용.
remotion-slide-compare
by Canine89Remotion 슬라이드의 compare 타입 전용 스킬. 2~4개 항목을 나란히 비교하는 슬라이드를 설계하거나 수정할 때 사용.
remotion-slide-evolution-flow
by Canine89Remotion 슬라이드의 evolution-flow 타입 전용 스킬. 왼쪽 상태에서 오른쪽 상태로 무엇이 어떻게 바뀌었는지, 그 변화 축과 결과를 3열 흐름으로 설명할 때 사용.
remotion-slide-pdf
by Canine89슬라이드를 PDF로 내보내기. 각 슬라이드를 PNG로 렌더한 뒤 pdf-lib으로 합쳐서 단일 PDF 파일을 생성한다. "PDF로 뽑아줘", "PDF 내보내기" 등의 요청에 사용.
remotion-slide-quote
by Canine89Remotion 슬라이드의 quote 타입 전용 스킬. 핵심 문장이나 인용구를 큰따옴표 장식과 함께 크게 보여주는 슬라이드를 설계하거나 수정할 때 사용.
remotion-slide-split
by Canine89Remotion 슬라이드의 split 타입 전용 스킬. 이미지와 불릿 설명을 좌우 또는 상하 분할로 구성하는 슬라이드를 설계하거나 수정할 때 사용.
remotion-slide-stat
by Canine89Remotion 슬라이드의 stat 타입 전용 스킬. 큰 숫자와 짧은 설명으로 임팩트를 주는 슬라이드를 설계하거나 수정할 때 사용.
remotion-api
by Canine89Remotion 프레임워크 API 레퍼런스. 핵심 API, 트랜지션, Player, 렌더링, 성능 최적화 가이드. 컴포넌트 작성·애니메이션·렌더링 시 참조.
remotion-slide-title-bullets
by Canine89Remotion 슬라이드의 title-bullets 타입 전용 스킬. 큰 제목과 불릿 리스트 중심의 설명 슬라이드를 설계하거나 수정할 때 사용.
remotion-slide-title-image
by Canine89Remotion 슬라이드의 title-image 타입 전용 스킬. 큰 제목과 단일 이미지를 조합하는 슬라이드를 설계하거나 수정할 때 사용.
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.