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...
forge-check-security
by moongci38-oss코드베이스 보안 취약점 자동 스캔 스킬. 15-phase CSO 심층 감사(OWASP Top 10 + CI/CD + STRIDE + 익스플로잇 패턴 + 트렌드). 입력: 프로젝트 루트 경로(기본값 CWD). 출력: docs/qa/security-report.md (CRITICAL/HIGH/MEDIUM/LOW 등급 리포트 + PASS/WARN/FAIL 판정). QA Phase 1 T6(보안 WARN 게이트)로 자동 트리거되거나 /forge-check-security 직접 호출. 트리거: QA T6 단계, PR 생성 전 보안 게이트, 수동 보안 검수.
benchmark
by moongci38-ossPR 생성 전 develop 대비 feature 브랜치의 성능을 비교하는 스킬. 번들 크기, 테스트 시간, API 응답 시간을 측정. P6 PR 생성 전 자동 트리거.
canary
by moongci38-ossdevelop/staging 통합 후 15분 헬스 모니터링을 수행하는 스킬. 에러율, 응답 시간, 메모리 사용량 추적. P6-DI PASS 후 자동 트리거.
codex-review
by moongci38-ossOpenAI Codex (gpt-5.5)를 경유한 2차 리뷰 게이트. Claude 1차 리뷰는 그대로 유지하고 동일 모델 맹점을 보완. Stage 분기 (plan/code/test/final/bugfix) · AUTO_STAGES 환경변수 정책 · OAuth 모드 비용 0 · P3/P4/P5/P6 자동 호출. 자동 트리거 시점 - Spec 또는 Plan 작성 후 (stage=plan, blocking), 코드 변경 PR (stage=code, 권고), E2E 시나리오 작성 후 (stage=test, 권고), PR 머지 직전 통합 검수 (stage=final, blocking high-effort), 버그 수정 patch 후 (stage=bugfix, 수동). 본 절차 실행은 ~/forge/.claude/commands/codex-review.md를 참조한다.
game-qa
by moongci38-ossUnity 게임 클라이언트 + 게임 서버 QA 자동화 (AD-93 W5: Phase A~H 정합). GodBlade/바둑이/맞고 전용. Unity MCP run_tests + .NET bot 빌드 + 소켓 스모크 + C# 정적분석. /qa Phase A~H와 동일 패턴 — 자동 브랜치 / bug-report 6하원칙+Failure Attribution / healer 라우팅 / cr-* / develop 자동 머지.
yt
by moongci38-ossPerforms end-to-end YouTube video analysis: extracts transcript + comments + description links via yt-analyzer.py, runs AI analysis with critical evaluation, fact-checking, web research, GTC verification (4-step), and ACHCE-tagged system improvement proposals. Supports multiple formats (summary/timeline/mindmap/full/blog) and parallel multi-video analysis. Use with any YouTube URL.
qa
by moongci38-ossQA 전 사이클 오케스트레이터 (AD-93 Phase A~H). 브랜치 생성→시나리오 전수→버그 발견→수정 계획→Healer 병렬→cr-* + Codex 검증→develop 자동 머지→Wiki 축적. 스코프 지정 가능 (--scope=full|domain|file-pattern).
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.