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...
file-maker
by berrzebbGenerate structured files (PDF, PPTX, DOCX, XLSX, ZIP) using Python inside a container sandbox. Use when the user asks to create, export, or generate a document, report, spreadsheet, presentation, or archive. After creation, use file-delivery skill to send the file. When explicitly requested, can also package multiple files into a ZIP archive before delivery. Do NOT use for plain text/markdown/CSV (use write_file directly), diagrams (use diagram skill), or viewing/reading existing files. Do NOT create ZIP unless user explicitly asks for it.
weather
by berrzebbGet current weather conditions and forecasts for any location using Open-Meteo API with KMA (Korea Meteorological Administration) data. No API key required. High-resolution 1.5km model for Korea. Use when the user asks about weather, temperature, rain, snow, humidity, wind, or forecasts. Do NOT use for historical weather data or climate analysis.
rolepm
by berrzebb기획 전담. 요구사항 분석, 스펙 작성, 우선순위 결정. Use when 복잡한 작업의 기획/분석이 필요할 때. Do NOT use for 직접 코드 작성, 직접 실행 — PL에게 위임.
summarize
by berrzebbSummarize or extract text from URLs, local files, YouTube videos, and podcasts using the summarize CLI. Use when the user asks to summarize a link, article, video, or document. Supports multiple output lengths and models. Do NOT use for web search (use web-search) or for content the agent can read directly.
roleconcierge
by berrzebb사용자 직접 대면. 일상 작업 처리, 개발 작업 감지 시 PM/PL에 위임. Use when 일반 질문, 검색, 정보 정리, 번역 등 비개발 작업. Do NOT use for 직접 코드 작성/수정 — 개발 작업은 PM/PL에 위임.
agent-browser
by berrzebbWeb search and browser automation using web_search, web_fetch, and web_browser tools. Use when: internet lookup, latest/real-time information, cross-source verification, any URL mentioned by user, dynamic page interaction, web crawling, site analysis, news search, price check, documentation lookup. Do NOT use for local file operations or tasks requiring no internet access.
tmux
by berrzebbRemote-control tmux sessions for interactive CLIs by sending keystrokes and scraping pane output. Use when the task needs an interactive TTY: Python REPLs, long-running processes, or orchestrating multiple agents in parallel. Requires tmux on PATH (macOS/Linux, WSL on Windows). Do NOT use for simple non-interactive commands (use just-bash).
rolepl
by berrzebb실행 조율 전담. 개발팀 spawn, 진행 감독, Phase Gate 판정, 결과 검증. Use when PM 스펙 수신 후 개발 실행이 필요할 때. Do NOT use for 기획/스펙 작성 — PM 역할.
hwpx
by berrzebb한글(HWPX) 문서 생성/읽기/편집. .hwpx 파일, 한글 문서, Hancom, OWPML 관련 요청 시 사용. 컨테이너(sandbox) 안에서 Python + lxml로 실행. Do NOT use for .hwp(바이너리) — .hwpx만 지원.
diagnose
by berrzebbSystematically diagnoses build, runtime, DB, API, and frontend errors with classification and fix workflow. Use when encountering errors or test failures.
consensus-loopstatus
by berrzebbShow current consensus-loop status — pending reviews, audit state, retro marker, agent assignments. Use to check what's happening before starting work or after returning from a break.
consensus-loopretrospect
by berrzebbExtract learnings from audit history and conversation, manage memories, clean up stale entries. Use after completing a track, during retrospective (③ memory step), at end of session, or anytime the user wants memory maintenance. Triggers on 'what did we learn', 'memory cleanup', 'review learnings', 'retrospective', 'update memories', '회고', '메모리 정리'.
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.