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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tfx-remote-spawn
by tellanglegacy thin alias. Phase 4b부터 spawn/list/attach/send/resume/kill/probe 공용 진입점은 tfx-remote 이다.
tfx-remote-setup
by tellanglegacy thin alias. Phase 4b부터 setup 관련 공용 진입점은 tfx-remote setup 이다.
tfx-autopilot
by tellangDEPRECATED — tfx-auto 로 통합됨. `/tfx-auto` 로 리다이렉트 (플래그 없음, 기본 동작 동일). Phase 5 (v11) 에 물리 삭제 예정. tfx-autopilot 은 tfx-auto 복제본이었으므로 플래그 없이 그대로 리다이렉트.
tfx-consensus
by tellangDEPRECATED — tfx-auto consensus root 로 통합됨. `/tfx-auto --mode consensus` 가 canonical entrypoint 다. Phase 4a 부터 consensus/debate/panel 은 같은 엔진 family 를 공유하고, 차이는 `--shape` 와 renderer 에만 남긴다. Phase 5 (v11) 에 물리 삭제 예정.
tfx-forge
by tellang새 스킬을 만들거나 기존 스킬을 수정할 때 사용한다. 'forge', '스킬 만들기', 'create skill', '새 스킬', '스킬 생성', 'SKILL.md 작성' 같은 요청에 반드시 사용. 반복 작업을 스킬로 자동화하고 싶을 때 적극 활용.
tfx-fullcycle
by tellangDEPRECATED — tfx-auto 로 통합됨. `/tfx-auto --mode deep --parallel 1` 로 리다이렉트. Phase 5 (v11) 에 물리 삭제 예정. "pipeline-thorough 단일 실행" 의미는 플래그로 동일 표현.
tfx-ralph
by tellangtfx-persist의 별칭(alias). 'ralph', '끝까지 해', '멈추지 마' 같은 요청에 사용. 실제 동작은 tfx-persist 스킬이 수행합니다. Use when: ralph, 끝까지, don't stop, 멈추지 마
tfx-multi
by tellang멀티-CLI 팀 모드. Claude Native Agent Teams + Codex/Antigravity 멀티모델 오케스트레이션.
tfx-research
by tellang웹 검색/리서치가 필요할 때 사용한다. '검색해줘', '찾아봐', '최신 정보', '이거 뭐야', '심층 조사', '자세히 알아봐', 'deep research', '전면 리서치', '자율 리서치', '조사해', 'research and plan' 같은 요청에 반드시 사용. 추가로 'X 있나?', 'X 쓸 수 있나?', 'X 풀려있어?', 'X 어떻게 쓰는지', 'X 가능한가?', '방법 있나?', 'X 살아있나?' 같은 도구·기능 존재/사용법/상태 의문문에도 사용.
tfx-review
by tellang코드 리뷰가 필요할 때 사용한다. 'review', '리뷰해줘', '코드 봐줘', '이거 괜찮아?', 'PR 리뷰', '변경사항 확인', '꼼꼼히 리뷰', 'deep review', '심층 리뷰', '보안까지 리뷰', '다각도 리뷰' 같은 요청에 반드시 사용.
tfx-setup
by tellangtriflux 초기 설정 및 진단. AskUserQuestion 기반 인터랙티브 위저드로 파일 동기화, HUD 설정, Codex 프로파일, CLI 진단, MCP 확인, 검색 MCP 설정을 수행합니다. Use when: setup, 설정, 설치, install, 초기화, 처음, 시작, wizard
tfx-ship
by tellangtriflux 전용 릴리즈 자동화. **GitHub Actions(release.yml dispatch / npm-publish.yml) 기반 CI 릴리즈가 기본 경로 — npm publish 는 OIDC Trusted Publishing 으로 CI 가 수행하므로 로컬 npm login 불필요.** scripts/release/* 래퍼 + AskUserQuestion 기반 버전 선택 + CHANGELOG 편집 게이트 + Co-Authored-By/AI trailer 금지 강제. 'ship', '배포', '릴리즈', 'release', 'tfx-ship', 'publish' 같은 요청에 반드시 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.