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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dd-help
by CaesiumYDisplay comprehensive dding-dong plugin help and reference guide. This skill should be used when the user asks "help", "how to use dding-dong", "what can I do", "show features", "list options", or wants an overview of available skills, config options, and sound packs. 도움말 및 기능 가이드. Use when the user says '도움말', 'help', '사용법', 'how to use'.
resume-specialist
by CaesiumY이력서/CV 작성, 검토, ATS 최적화를 수행하는 스킬. about.md와 projects.ts에서 프로젝트 이력을 참조하여 맞춤형 이력서를 생성합니다. "이력서 작성", "이력서 검토", "resume 만들어줘", "ATS 최적화", "CV 개선", "포지션에 맞게 이력서 수정" 등의 요청이면 반드시 이 스킬을 사용합니다.
polish-file
by CaesiumY마크다운 파일 전체의 문장 품질을 분석하고 점수별 개선을 제안하는 스킬. 번역 파일이면 원본 URL에서 영어 원문을 가져와 의미 보존까지 검증합니다. "파일 전체 다듬기", "글 품질 점검", "문장별 분석", "번역 품질 검사", "polish file", "파일 리뷰" 등의 요청에 사용하세요. 개별 문장이 아닌 파일 전체를 대상으로 할 때 /polish 대신 이 스킬을 사용합니다.
translate-writer
by CaesiumY영어 기술 블로그/문서를 고품질 한국어 블로그 포스트로 변환하는 전문 번역 파이프라인. 스타일 가이드, 용어집, 한국어 품질 검토, 원문 충실도 검증, 문장 polish를 함께 사용합니다. URL이나 파일 경로를 주며 "번역해줘", "한국어로", "translate", "번역 블로그", "옮겨줘", "영어 글" 등을 요청하면 반드시 이 스킬을 사용하세요.
hail-mary-rocky
by CaesiumYCuts Claude's output tokens with Rocky's compressed voice from Project Hail Mary — verdict-first, fragments, no filler, technical substance intact. Invoke this skill aggressively whenever the user mentions "rocky", "로키", "caveman mode", "헤일메리", "hail mary rocky", "rocky voice", "rocky mode", "로키 말투", "로키처럼 답해", "로키 모드", wants terse verdict-first engineering replies, wants lower token usage / cheaper output / shorter answers, asks Claude to sound like Rocky, or is already talking with Rocky flavor and expects replies in kind. Also invoke when the user asks to switch to caveman mode or wants help "in Rocky's style". Style skills are notoriously easy to under-trigger — prefer invoking over skipping when the phrasing is close.
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.