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...
vue-router
by a35506322當使用 Vue Router 時運用此技能，裡面包含 Vue Router 最佳實踐規則、Navigation Guard 最佳實踐規則、Error Handling 最佳實踐規則等
uiux-dev
by a35506322在編寫 UIUX 設計時運用此技能。
mstest-unit-test
by a35506322適用於 MSTest 單元測試最佳實踐模式，裡面包含 MSTest 單元測試、NSubstitute Mock、Entity Framework Core InMemory 等
api
by a35506322適用於 API 專案最佳實踐模式，裡面包含 API 垂直切割、DataBase Optimization、Log、ExceptionHandler、Endpoint Open API、Adapters (第三方 API 封裝) 等
axios
by a35506322適用於編寫 Axios 時最佳實踐。
backend-coverage-report
by a35506322產生後端專案的覆蓋率測試報告。使用時機：使用者要求產生覆蓋率報告、檢視測試覆蓋率、或討論 coverage 時。
backend-tdd-workflow
by a35506322在編寫後端專案新功能、修復錯誤或重構程式碼時運用此技能。強制執行測試驅動開發，包含單元測試與整合測試。
create-api-spec
by a35506322從單一 API endpoint 程式碼產出規格文件（Markdown）。使用時機：使用者給你 endpoint 程式碼路徑要求產出 API spec，例如「把 XxxEndpoint.cs 寫成 spec」「幫這個 controller 產出 API 文件」「產 spec.md」。支援任何後端框架（.NET / Node / Python / Java 等），流程框架無關。
create-feature-todo
by a35506322從 logic flow 文件（`docs/features/*.md`，由 map-feature-flow 產出）抽出「功能 todo 清單」，輸出到 `docs/todo/<name>.md`。所有項目預設未勾選 `[ ]`，作為實作完成度快照；後續使用者會帶對標檔案請你比對打勾，但本 skill 只負責產初始清單。使用時機：使用者給你 logic flow 路徑要求產 todo，例如「幫 docs/features/todo.md 產 todo 清單」「把 login flow 變成 todo」。
create-mermaid
by a35506322依據使用者描述繪製 Mermaid 流程圖。當使用者要求建立流程圖、步驟圖、邏輯圖或 Mermaid 語法圖時使用。
folder-structure
by a35506322依照使用者指定的檔案與資料夾輸出樹狀結構，並可在節點旁加上註解說明。當使用者要求產生專案結構、目錄樹或檔案架構時使用。
frontend-dev-workflow
by a35506322在編寫前端專案新功能、修復錯誤或重構程式碼時運用此技能。
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.