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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organize-claude-md
by bigbulgogiburgerCLAUDE.md를 Lazy Loading 참조 구조 + 프레임워크 특화 템플릿 + Mermaid 아키텍처로 재구성. Monorepo 분기 + CHANGELOG 분리 + ADR 자동 생성 안내. **반드시 사용:** "CLAUDE.md 정리", "CLAUDE.md 재구성", "claude.md 비대화", "오늘 개발 사항 정리", "Last Updated 정리", "참조 문서 만들어줘", "reference docs", "monorepo CLAUDE 분리", "프로젝트 문서 organize", "AI agent 문서 구조 개선", "organize claude md" 등의 요청. CLAUDE.md 가 120줄(단일) / 150줄(monorepo root) 초과, Last Updated 라인이 3K 토큰 또는 5회 closure 누적, sub-project 추가 후 root 구조 갱신, 기존 reference 가 구식화된 경우에도 트리거. 단일 프로젝트 / monorepo 모두 지원. Spring Boot (Java/Kotlin) / Vue / Nuxt / React / Next.js / Flutter / NestJS / FastAPI / Django / Go 프레임워크별 특화 스캔.
dhelix-e2e-test
by bigbulgogiburgerE2E validation skill for the dhelix CLI AI coding assistant. Two test modes: (1) Project E2E — multi-turn tests creating real projects across 5+ tech stacks, validating builds, 80% coverage, and DHELIX.md compliance. (2) Conversation Quality — multi-turn tests verifying context retention, tool call coherence, error recovery, instruction adherence, progressive complexity, and contradiction handling. Use this skill when: running E2E tests for dhelix, validating coding ability across tech stacks, testing multi-turn conversation quality, verifying /init and DHELIX.md system, benchmarking dhelix against real-world project creation, or evaluating conversation quality after model/prompt changes.
create-pptx
by bigbulgogiburgerDB Inc. 브랜드 디자인 시스템(DB제목 B/M/L 폰트, 네이비 블루 테마)을 적용하여 전문적인 PPTX 프레젠테이션을 생성합니다. PPT, 프레젠테이션, 발표자료, 제안서, PoC 문서, 보고서 등을 만들어달라는 요청에 사용하세요. "PPT 만들어줘", "발표자료 작성", "프레젠테이션 생성", "pptx 제안서" 등의 요청에도 반드시 사용하세요.
graphify
by bigbulgogiburgerany input (code, docs, papers, images) → knowledge graph → clustered communities → HTML + JSON + audit report. Use when user asks any question about a codebase, project content, architecture, or file relationships — especially if graphify-out/ exists. Provides persistent graph with god nodes, community detection, and BFS/DFS query tools.
codebase-ai-readiness
by bigbulgogiburger임의의 git 레포지토리가 AI 코딩 에이전트(Claude Code, Cursor, Copilot, Aider 등)와 얼마나 잘 협업할 수 있는지 100점 7-카테고리 루브릭으로 감사하고 JSON 점수표, 한국어 HTML 대시보드, ROI 우선순위 액션 리스트를 산출합니다. 프레임워크 무관 — Python, JS/TS, Java, Go, Rust, Ruby 등 어떤 언어든 동작. Use this skill PROACTIVELY whenever the user asks about: - "AI-ready", "AI 준비도", "에이전트 준비도", "agent readiness", "codebase audit" - "이 코드베이스 점수 매겨줘", "AI 협업 가능한지 평가해줘", "코드베이스 감사" - "AGENTS.md 있는지 확인", "CLAUDE.md 점검", "에이전트 친화적인지" - "이 레포가 AI랑 얼마나 잘 맞는지", "Claude Code 쓰기 좋은 구조인지" - "리팩토링 우선순위", "ROI 기반 개선 항목", "어디부터 손대야 하나" - 새 레포에 합류했을 때 / 외부 OSS 평가 / 도입 가능성 검토 - 시니어 엔지니어가 팀 코드베이스 진단을 의뢰하는 모든 상황 Also trigger when the user mentions any of: Factory.ai Agent Readiness, AGENTS.md spec, agentic coding setup, dev environment scoring, repo health check, codebase quality benchmark.
verify-model-capabilities
by bigbulgogiburgerModelCapabilities 인터페이스, 모델 오버라이드, 클라이언트 파라미터 분기의 일관성 검증. LLM 관련 코드 또는 기본 모델 변경 후 사용.
jira-create
by bigbulgogiburgerjira-create — 자연어 한 줄 또는 문서를 읽어 Jira 이슈를 생성합니다. 단일 이슈는 바로 등록하고, 문서 기반이면 에픽→이슈→하위이슈 계층으로 일괄 등록합니다. 'jira 이슈 만들어줘', '지라 이슈 등록해줘', '이슈 등록', '이슈 생성', '에픽 만들어줘', '스토리 추가', '백로그에 추가', '이 문서 보고 이슈 등록해줘', 'w1-w4.md 지라에 올려줘', '계획 문서를 지라로 옮겨줘', 'jira create', '/jira-create' 등의 요청에 반드시 이 스킬을 사용하세요. 워크플로우의 시작점으로, /jira-start 이전에 사용합니다.
deploy-backend
by bigbulgogiburger백엔드 배포 (JAR 빌드 → SCP → Docker 재시작). 백엔드 배포, 서버 배포, 프로덕션 반영, backend deploy 요청 시 사용. 배포 명령어가 기억나지 않을 때도 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.