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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start
by openaiUse when starting Zoom work.
travel-planner
by revfactoryA full pipeline where an agent team collaborates to generate everything from destination analysis to local information for travel planning. Used for 'plan a trip', 'Japan travel itinerary', 'Europe backpacking', 'weekend getaway', 'international travel prep', 'travel budget', 'accommodation recommendations', 'travel itinerary', 'local restaurants', 'travel route recommendations', etc. Supports analysis or improvement of existing itineraries. Real-time flight/accommodation booking, visa application processing, and travel agency package comparisons are outside this skill's scope.
wedding-planner
by revfactorywedding preparation A pipeline where an agent team collaborates for comprehensive planning. 'wedding preparation ', ' ', 'wedding budget', 'wedding hall recommendation', 'studio/dress/makeup comparison', 'invitation document', 'wedding checklist', 'honeymoon plan', 'betrothal gifts preparation', 'trousseau list', 'wedding timeline' etc. wedding preparation before skill usage. item only neededKorean case also applicable departmentminute only degreeKRW. However, actual vendor exampleapprox. versus, processing, marriage registration versus is outside this skill's scope.
budget-planning
by revfactoryevent budget plan framework. concept-planner and logistics-manager agent event budget establishand managementto do when reference. 'event budget', 'cost calculation', 'budget allocation' request when usage. However, total processing tax scope outside.
budget-calculator
by revfactory여행 예산을 항목별로 산출하고 절약 전략을 제시하는 예산 계산기. 'budget-manager' 에이전트가 여행 비용을 산출하고 예산을 배분할 때 이 스킬의 비용 기준표, 예산 배분 공식, 절약 팁을 반드시 활용해야 한다. '여행 예산 산출', '비용 비교', '절약 팁' 등에 사용한다. 단, 일정 설계나 현지 정보는 이 스킬의 범위가 아니다.
route-optimizer
by revfactory여행 동선을 최적화하고 이동 시간·비용을 최소화하는 경로 최적화 도구. 'itinerary-designer' 에이전트가 일별 동선을 설계할 때 이 스킬의 동선 최적화 알고리즘, 교통수단 비교 매트릭스, 시간 블록 배분 규칙을 반드시 활용해야 한다. '동선 최적화', '이동 경로', '교통수단 비교' 등에 사용한다. 단, 예산 계산이나 현지 정보는 이 스킬의 범위가 아니다.
travel-planner
by revfactory여행 계획의 목적지분석부터 현지정보까지 전 과정을 에이전트 팀이 협업하여 생성하는 풀 파이프라인. '여행 계획 짜줘', '일본 여행 일정', '유럽 배낭여행', '제주도 2박3일', '해외여행 준비', '여행 예산', '숙소 추천', '여행 일정표', '현지 맛집', '여행 코스 추천' 등 여행 계획·일정·예산 관련 요청에 이 스킬을 사용한다. 기존 일정이 있으면 분석이나 개선을 지원한다. 단, 항공권·숙소 실시간 예약 대행, 비자 신청 대행, 여행사 패키지 비교는 이 스킬의 범위가 아니다.
facilitation-techniques
by revfactory회의 퍼실리테이션 기법 라이브러리. agenda-architect와 followup-planner 에이전트가 효과적인 회의를 설계할 때 참조. '퍼실리테이션', '회의 진행 기법', '워크숍 설계' 요청 시 사용. 단, 실제 회의 진행이나 화상회의 도구 운영은 범위 밖.
budget-planning
by revfactory이벤트 예산 계획 프레임워크. concept-planner와 logistics-manager 에이전트가 이벤트 예산을 수립하고 관리할 때 참조. '이벤트 예산', '비용 산출', '예산 배분' 요청 시 사용. 단, 회계 처리나 세무 신고는 범위 밖.
team-offsite-planner
by mohitagw15856Plan a team offsite from goals to full agenda. Use when asked to plan a team offsite, away day, team retreat, quarterly offsite, or team-building event. Produces a full agenda, session designs, facilitation notes, and logistics checklist.
partnerklausur-design
by KlotzketteKlausurfahrplan mit Zahlen, Konfliktmoderation und Beschlussvorlagen. Für Managing Partner, Management Committee oder COO/CFO einer deutschen mittelständischen Kanzlei mit konkreten Abfragen, Kennzahlen, Entscheidungen und berufsrechtlicher Quellenhygiene im Kanzlei Management: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
eigentuemerversammlung-vorbereiten
by KlotzkettePlant eine Eigentümerversammlung (Stand 05/2026) von Themenstapel, Beschlussbedarf, Unterlagen, Beiratsabstimmung, Einladungszeitplan, Raum/virtueller Teilnahme bis zur Nachbereitung. Berücksichtigt § 23 Abs. 1a WEG, BGH-Linie zu Vorbefassung (V ZR 86/24), Abrechnung (V ZR 102/23) und Schlüsseländerung (V ZR 236/23, V ZR 128/23).
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.