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...
weapp-vite-website-curator
by weapp-viteMaintain and enrich weapp-vite website docs from packages source of truth. Use when updating `website/` content based on `packages/*` code or README, adding package docs pages, syncing VitePress nav/sidebar, or validating package-to-doc coverage after package changes.
wevu-best-practices
by weapp-vite面向小程序中 wevu 运行时的实践手册，覆盖生命周期注册、响应式更新、事件契约、`bindModel/useBindModel`、`setPageLayout/usePageLayout`、根入口 `useNativeRouter/useNativePageRouter`、`wevu/router`、store 约束，以及 `setData`、渲染、页面切换、资源与内存性能治理。
docs-and-website-sync
by weapp-vite面向采用 weapp-vite monorepo 布局仓库的文档、website 与 skills 同步工作流。适用于代码能力已变化但 website/docs/README/skills/AI 指南/packaged docs 可能过期的场景，尤其覆盖 `weapp` 配置页、`dist/docs`、`AGENTS.md` 模板、AI skills 安装、`prepare`、MCP、`forwardConsole`、`screenshot/compare/ide logs`、Web runtime、lib mode、多平台与 routeRules/layout 等入口同步。
playwright-cli
by weapp-viteAutomates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
weapp-vite-vue-sfc-best-practices
by weapp-vite面向使用 weapp-vite 的小程序项目的 Vue SFC 实践手册，覆盖 `<script setup lang="ts">`、JSON 宏、`definePageMeta`/layout、`defineModel`、`usingComponents`、模板指令兼容、`.weapp-vite` 类型支持文件、受管 `prepare` 工作流，以及和脚手架 `AGENTS.md` / 本地 `dist/docs` 对齐的当前 SFC 约定。
weapp-vite-best-practices
by weapp-vite面向采用 weapp-vite 项目布局仓库或已安装 `weapp-vite` 依赖项目的工程化实践手册，覆盖 `vite.config.ts` 的 `weapp` 配置、自动路由、routeRules/layout、自动导入组件、分包、npm、多平台、受管 TypeScript、`prepare`、`forwardConsole`、`mcp`、`screenshot/compare/ide logs`、Web runtime、lib mode、worker、`dist/docs`、脚手架 `AGENTS.md`、AI skills 安装，以及与 `weapp-ide-cli` 的命令治理和透传边界。
weapp-devtools-e2e-best-practices
by weapp-vite面向采用 weapp-vite monorepo 布局仓库的 WeChat DevTools runtime e2e 工作流。适用于 `e2e/ide/**`、`miniprogram-automator`、真实运行时页面断言、共享 automator 启动、`miniProgram.reLaunch(...)` 串联、`project.private.config.json` 条件页维护，以及和 `weapp-vite screenshot/compare/ide logs` 配合形成真实运行时验收链路。
release-and-changeset-best-practices
by weapp-vite面向采用 weapp-vite monorepo 布局仓库的 release、changeset 与 issue 交付工作流。适用于判断某次改动是否需要 changeset、是否联动 `create-weapp-vite`，以及从 issue 复现、worktree、回归覆盖到 PR 的仓库交付闭环。
native-to-weapp-vite-wevu-migration
by weapp-vite面向原生微信/支付宝/抖音小程序渐进迁移到 `weapp-vite + 原生` 或继续升级到 `weapp-vite + wevu + Vue SFC` 的结构化工作流，重点覆盖路线选择、工具链接入、原生保留、Vue SFC 试点、wevu 响应式升级、截图/日志/e2e 验证与 AI 维护约束。
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.