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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ai-article
by itwanger用于AI类内容的撰写。支持三种风格：安装教程类（手把手教学）、产品评测类（有观点有数据）、面试对话类（面试场景）。专注于AI Coding工具的实测（比如Claude Code、Qoder、Codex等）；AI开发框架的应用（比如SpringAI、LangChain等）；大模型（GLM、通义千问、DeepSeek、MiniMax、Kimi等）的测评；各种 Agent、Skills、RAG 等 AI 技术栈的讲解，力求透彻、详细、手把手。
changelog
by pipecat-aiCreate changelog files for important commits in a PR
paper-plan
by wanshuiyinGenerate a structured paper outline from review conclusions and experiment results. Use when user says \"写大纲\", \"paper outline\", \"plan the paper\", \"论文规划\", or wants to create a paper plan before writing.
systems-paper-writing
by Orchestra-ResearchComprehensive guide for writing systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys. Provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, reviewer guidelines, LaTeX templates, and conference deadlines. Use this skill for all systems conference paper writing.
prompt-master
by nidhinjsGenerates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other non-prompt-engineering work.
create-blog-post
by home-assistantUse this if the user wants to convert a blog post from Google Docs markdown to the format used in the Home Assistant website.
postgres-commit-history-article
by digoalAnalyze PostgreSQL git commit history between two commit ids and, when valuable DBA/application-developer changes exist, write a Chinese Markdown article for WeChat/公众号 readers with examples checked against SQL tests, docs, or relevant source. Use when the user is inside a PostgreSQL source checkout and provides two commit ids, asks to interpret Postgres commits, summarize a release/development window, identify DBA/application-developer value from commit logs, group related commits, exclude reverted commits, and either output a sourced article under the current project's markdown directory or return a no-article analysis when no valuable commits are present.
summarization
by UpsonicSummarize documents, articles, conversations, code, and technical content into concise, accurate summaries. Use when user asks to summarize, condense, create a TL;DR, write an executive summary, extract key points, or distill content. Trigger when user says things like "summarize this", "give me the key points", "TL;DR", "what are the main takeaways", "condense this", "brief me on this", or provides long content asking for a shorter version. Also trigger for meeting notes summaries, research paper abstracts, and changelog summaries. Do NOT trigger for rewriting or paraphrasing at similar length, translation, or content generation from scratch.
documentation-templates
by vudovnDocumentation templates and structure guidelines. README, API docs, code comments, and AI-friendly documentation.
dbs-content-system
by dontbesilent2025dontbesilent 内容结构化系统。把本地大量文稿、推文、选题、案例和课程稿搭成一个可持续生长的内容结构化工程：先审计内容规模与边界，再建立新工程、复制素材、抽取内容单元、生成主题地图与选题装配稿。 触发方式：/dbs-content-system、/内容结构化系统、「把我的内容做成结构化系统」「把本地素材变成可重组系统」「帮我搭内容资产工程」「我想把旧内容变成可复用资产」 Content structuring system. Audits local content volume, then builds a reusable content knowledge project with units, topic maps, and assembly drafts. Trigger: /dbs-content-system, "build a content structuring system", "turn my archive into reusable assets"
content-redirect
by microsoftCreate and manage redirects in VS Code documentation when pages are moved, renamed, or deleted. Use when moving docs pages, renaming files, restructuring content, or when the user asks about redirects.
docs-guidance
by petyosiRevises project documentation for human readability, conceptual flow, and example safety. Covers concept pages, example pages, troubleshooting, installation, and migration docs. Use when writing or reviewing docs, when the user says a page "feels off", "feels weird", "feels sudden", or "needs attention", when asked to make a doc clearer or to rewrite a section, when fixing cold or abrupt openings, when a section opens with the API mechanism instead of the user's problem, or when comparing prose against a readability standard.
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.