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...
next-build-node-runtime
by ai-shifu当 cook-web 执行 npm run build 出现 SyntaxError Unexpected token '?' 或 next/dist/compiled 报错时使用本技能，优先排查 Node 运行时版本不一致问题。
chat-layout-width-detection
by ai-shifu当修复 ai-shifu 聊天页在移动端与桌面端布局判定不一致的问题时使用本技能。基于真实可见视口宽度而不是仅 `#root.clientWidth` 计算 `frameLayout`，在 `resize` 与 `visualViewport.resize` 时同步，并在断点纠正后关闭过期的移动端抽屉状态。
deep-link-lessonid-routing
by ai-shifu当 cook-web 需要按 URL 深链定位课节并保持学习端与后台端行为一致时使用本技能。统一使用 lessonid 参数、复用目录点击拦截链路，并覆盖登录/付费/无效课节兜底。
fullscreen-dialog-portal
by ai-shifu当 cook-web 页面在浏览器 fullscreen 场景下需要展示基于 Dialog 的支付弹窗、设置弹窗或业务弹层时，使用本技能排查 portal 容器是否落在全屏节点外。
async-confirm-dialog-loading
by ai-shifu当 cook-web 的确认弹窗会触发重修、删除、恢复等异步请求时，使用本技能保证按钮防重复点击、loading 状态与弹窗关闭时机和请求完成保持一致。
chat-element-streaming
by ai-shifu当 ai-shifu 聊天流从 block 粒度向 element 粒度演进，或历史记录与 SSE 渲染一致性出现问题时使用本技能。统一 element_bid 渲染键、兼容旧字段并收敛 AskBlock 归并逻辑。
gen-mdf-proxy
by ai-shifuUse when changing the backend MDF proxy, request validation, timeout handling, or frontend/backend ownership for MDF conversion. Keep the external service hidden from the browser.
shifu-authoring-flow
by ai-shifuUse when changing backend shifu authoring, draft history, publish, outline structure, or import/export behavior. Keep draft and publish flows consistent and protect outline integrity.
user-auth-flows
by ai-shifuUse when changing backend user auth, verification codes, token persistence, temp-user behavior, or auth-provider integration. Keep provider dispatch and credential state centralized.
app-error-boundary-display
by ai-shifu当 cook-web 需要调整 Next.js App Router 全局或路由级错误兜底页时使用本技能。错误页应直接展示错误名称、message、digest、cause、URL 和可用 stack，避免只提示用户查看控制台。
chat-actionbar-ask-placement
by ai-shifu当调整聊天操作栏、追问入口和 AskBlock 锚点时使用本技能。确保内容与操作入口同步出现，避免双输入框、空菜单和错位展示。
hook-contract-refactor-safety
by ai-shifu当重构 hook 返回字段或参数契约时使用本技能。统一同步调用方解构与参数对象，避免大面积 TS 属性错误。
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.