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...
code-review-skill
by awesome-skillsProvides comprehensive code review guidance for React 19, Vue 3, Angular 17+, Svelte 5, Rust, TypeScript, Java, PHP, Python, Django, Go, C#/.NET, Kotlin, Swift, NestJS, C/C++, and more. Helps catch bugs, improve code quality, and give constructive feedback. Use when: reviewing pull requests, conducting PR reviews, code review, reviewing code changes, establishing review standards, mentoring developers, architecture reviews, security audits, checking code quality, finding bugs, giving feedback on code.
first-principles-thinking
by awesome-skillsThis skill should be used when the user asks to "analyze from first principles", "第一性原理", "从根本分析", "从零开始思考", "think from scratch", "question this design", "这个设计合理吗", "is this the right approach", "为什么要这样做", "why are we doing it this way", "有没有更好的方案", "is there a better solution", "challenge assumptions", "挑战假设", or needs to evaluate architectural decisions, design choices, or problem-solving approaches without relying on analogies or conventions.
mobile-app-design-standards
by awesome-skillsThis skill should be used when the user asks to "design mobile UI", "review app design", "check UI guidelines", "improve app UX", "design React Native interface", "create app screens", "follow design standards", or mentions iOS/Android design patterns, accessibility, or mobile user experience. Provides comprehensive mobile app UI/UX design guidance.
5-whys-root-cause-analysis
by awesome-skillsThis skill should be used when the user asks to "find the root cause", "找根因", "为什么会出现这个问题", "why did this happen", "debug this issue", "排查问题", "analyze this bug", "分析这个bug", "what's causing this", "问题出在哪", "dig deeper", "深挖原因", or needs to systematically trace a problem back to its fundamental cause rather than just addressing symptoms.
mermaid-syntax
by awesome-skillsThis skill should be used when the user asks to "create a mermaid diagram", "fix mermaid error", "mermaid syntax error", "diagram not rendering", "flowchart not working", "sequence diagram broken", "escape special characters in mermaid", or mentions "mermaid", "flowchart", "sequence diagram", "class diagram", "state diagram", "ER diagram", "gantt chart". Prevents common syntax errors with special characters, reserved words, escaping rules, and provides v11 syntax support.
manim
by awesome-skillsThis skill should be used when the user asks to "create an animation", "make a manim video", "animate this concept", "visualize this process", "create a GIF for my blog", "plot a graph", "animate a value", or mentions "manim", "mathematical animation", "code animation", "process visualization", "technical animation", "3D scene", "camera animation", "ValueTracker", "number animation". Provides ManimCE (Community Edition) syntax, patterns, and best practices for creating programmatic animations.
insights
by awesome-skills分析当前 agent（Claude Code / Codex / Gemini CLI / OpenCode）的本地会话记录，生成一份 HTML 使用洞察报告，包含项目领域、互动风格、亮点、摩擦点、改进建议、机会展望。当用户说 "看下我最近用 agent 的情况"、"生成 insights"、"分析我的会话历史"、"我的使用模式"、"agent usage report"、"/insights"、"analyze my sessions"、"使用洞察"、"复盘最近用 AI 的情况" 时使用本 skill。即使用户没明说 "insights" 也要触发：只要请求涉及总结/复盘/分析本机 agent 历史、对话记录、commit 模式、工具使用频率，就用这个。
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.