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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mcp-builder
by ProjectEuropaGuide for creating MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Triggers: 「MCPサーバーを作成」「外部API連携」「ツール統合」「MCP開発」 Use when: Building MCP servers to integrate external APIs or services. Recommended stack: TypeScript + MCP SDK
wcag-audit-patterns
by ProjectEuropaConduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. Use when auditing websites for accessibility, fixing WCAG violations, or implementing accessible design patterns.
web-artifacts-builder
by ProjectEuropaSuite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
webapp-testing
by ProjectEuropaTest and verify local web applications using browser automation. Triggers: 「ブラウザでテスト」「UIの動作確認」「スクリーンショット」「実際に動かして確認」 Use when: Verifying frontend functionality, debugging UI behavior, capturing screenshots. Uses AntiGravity's browser_subagent for browser interaction.
canvas-design
by ProjectEuropaCreate beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
doc-coauthoring
by ProjectEuropaGuide users through a structured workflow for co-authoring documentation. Triggers: 「ドキュメントを書いて」「仕様書を作成」「READMEを更新」「提案書を作って」「PRD」「設計ドキュメント」 Use when: Writing documentation, proposals, technical specs, decision docs, or similar structured content. Workflow: Context Gathering → Refinement & Structure → Reader Testing
e2e-debug
by ProjectEuropaAnalyze and fix failing E2E tests. Includes CI auto-repair mode for GitHub Actions failures. Triggers: 「テストが失敗」「CIが落ちている」「テストをデバッグ」「test failed」「flaky test」 Use when: E2E tests are failing, tests are flaky/unstable, error messages need investigation. Outputs: Fixed Page Object and spec files, error analysis reports.
e2e-refactor
by ProjectEuropaRefactor existing E2E tests to use Page Object Model pattern and semantic locators. Triggers: 「テストをリファクタリング」「ロケータを改善」「Page Objectに変換」「refactor test」 Use when: Tests don't use semantic locators, Page Object pattern not applied, tests use deprecated patterns. Outputs: Updated/new Page Objects and refactored spec files.
e2e-write
by ProjectEuropaCreate new E2E tests using Playwright with Page Object Model pattern and semantic locators. Triggers: 「E2Eテストを書いて」「テストを作成して」「新機能のテストが必要」「E2E test」「create test」 Use when: Creating E2E tests for new pages or components, after implementing new features. Outputs: Page Object files (frontend/e2e/pages/) and spec files (frontend/e2e/*.spec.ts)
frontend-design
by ProjectEuropaCreate distinctive, production-grade frontend interfaces with high design quality. Use when building web components, pages, or applications. Generates creative, polished UI that avoids generic "AI slop" aesthetics. Triggers: 「UIを作成」「コンポーネントを作って」「美しいページを」「デザインを改善」 Project stack: Next.js 16 + React 19 + TailwindCSS 4 + shadcn/ui
nextjs-app-router-patterns
by ProjectEuropaMaster Next.js 16+ App Router with Server Components, streaming, parallel routes, and advanced data fetching. Use when building Next.js applications, implementing SSR/SSG, or optimizing React Server Components.
react-best-practices
by ProjectEuropaReact and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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.