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.
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omd-add-reference
by kwakseongjaeURL 또는 brand id 입력 → 3-tier 검증 파이프라인(Tier 1 라이브 + Tier 2 getdesign/refero + Tier 3 reconcile)으로 references/<id>/DESIGN.md를 신규 생성(CREATE)하거나 기존 섹션을 검증·갱신(UPDATE). '레퍼런스 추가/수정', 'X DS 검증', 'X 컴포넌트 다시 뽑아줘' 류에 트리거.
omd-add-reference
by kwakseongjaeURL 또는 brand id 입력 → 3-tier 검증 파이프라인(Tier 1 라이브 + Tier 2 getdesign/refero + Tier 3 reconcile)으로 references/<id>/DESIGN.md를 신규 생성(CREATE)하거나 기존 섹션을 검증·갱신(UPDATE). '레퍼런스 추가/수정', 'X DS 검증', 'X 컴포넌트 다시 뽑아줘' 류에 트리거.
omd-design
by kwakseongjae사용자와 대화하며 디자인 선호도를 파악하고, oh-my-design 서베이를 통해 DESIGN.md를 생성합니다. '디자인 시스템 만들어줘', 'DESIGN.md 생성', '디자인 잡아줘', 'UI 스타일 정해줘' 등 디자인 시스템 구성이 필요할 때 트리거됩니다.
omd-token-backfill
by kwakseongjae기존 references/<id>/DESIGN.md(엄격히 작성된 산문)에서 머신리더블 `tokens:` 블록(DTCG-lite: colors/typography/rounded/spacing/shadow/components)을 역추적(prose-derived)해 frontmatter에 backfill. token↔prose 정합성 게이트로 검증하고 token-status 체크리스트를 갱신. 배치(기본 10개)로 며칠에 나눠 실행. '토큰 백필', 'tokens 블록 채워', 'X에 토큰 추가', '토큰 배치 돌려', '남은 레퍼런스 토큰화' 류에 트리거. 신규 reference의 토큰은 omd-add-reference Phase 4.5가 담당(여긴 기존 ref 전용).
omd-batch-launch
by kwakseongjae10개 brand 일괄 reference 추가 + 문서 카운트 sync + hyperframes 기반 promo MP4 생성. '10개씩 추천해줘', '한국 IT 10개 추가하고 영상까지', 'batch-launch', 'X 카테고리에서 10개 더' 류 트리거. 매 회 reference count +10, gitignored promo video 1편.
omd-codex-image
by kwakseongjae이미지 placeholder를 동적으로 materialize. Codex 채널에서는 내장 image-generation primitive 호출, Claude Code 채널에서는 omd-asset-curator로 fall back, OpenCode에서는 spec dump. HTML/MD의 `<!-- omd:gen-image -->` 블록을 단일 source of truth로 사용. '이미지 생성해줘', '플레이스홀더 채워줘', '코덱스로 이미지 만들어' 류 트리거.
omd-component-harvest
by kwakseongjae기존 references/<id>의 §8 Component Patterns + frontmatter `tokens.components`를 멀티서피스(여러 라우트 + 메뉴/모달 인터랙션) + 공개 디자인시스템(Storybook/Primer/Polaris/Cedar/Geist/*.design) 크롤로 풍부화. 단일 랜딩 스냅샷의 '버튼 수준'을 모달·탭·테이블·토스트·폼상태까지 확장하되, 소스가 빈약하면(랜딩만 있는 앱 중심 기업) 억지로 만들지 않고 정직하게 cap. 완료 시 `tokens.components_harvested: true` 마커. '컴포넌트 풍부화', '컴포넌트 하베스트', 'X 컴포넌트 보강', 'component harvest' 류에 트리거. 토큰 자체가 없으면 먼저 omd-token-backfill/omd-add-reference.
omd-designer-review
by kwakseongjae시각 + 브랜드 일관성 리뷰. HTML/MD/JSX artifact를 받아 brand DESIGN.md 대비 typo hierarchy, 색 budget, radius scale, 컴포넌트 state, 모바일 반응형 검수. severity BLOCK/WARN/FYI + line ref 출력. 'UI 리뷰', '디자인 검토', 'DESIGN.md 대비 검수' 류 트리거.
omd-final-qa
by kwakseongjae출간 ready 직전 read-only critic. 8-item rubric 강제. 'looks good' rubber-stamp 금지. 라인 ref 필수. 2-round revision hard cap. '최종 QA', '출간 검수', 'rubric으로 평가' 류 트리거.
omd-kr-writer
by kwakseongjae한국어 글쓰기 멀티-preset 스킬. 12개 preset 지원 — toss-tech-design (default) / karrot-neighborly / brunch-maker-popular / naver-d2-engineering / biz-formal-report / academic-paper / journalism-broadsheet / kakao-warm-product / line-global-saas / academic-lecture-essay / emotional-brand / legal-disclosure. '한글 글 작성', 'KR rewrite', '토스 톤으로', '당근 톤으로', '브런치 톤으로', '보고서로 써줘', '학술 톤', '신문기사체' 류 트리거.
omd-lab-01-designmd-impact
by kwakseongjaeOmD Lab #01 — DESIGN.md 유무·생성방식이 UI 결과물에 미치는 영향을 4가지 조건으로 비교. 토스 스타일 모바일 UI를 (V1) DESIGN.md 없이, (V2) 수동 작성, (V3) 자동 생성, (V4) 자동 생성 + 5회 피드백 루프로 만들어 동시 비교 뷰로 시각화. 사용자가 본인의 Claude Code 안에서 재현 가능한 실험.
omd-locale-adapter
by kwakseongjae한국어 본문을 EN/JP/ZH-TW로 **번역이 아닌 adaptation**. 문화 레퍼런스 swap, JP honorific 정합, ZH-TW 번체 idiom. KR은 항상 source of truth. '다국어 적용', 'EN 버전 만들어줘', 'JP로 옮겨줘' 류 트리거.
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.