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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cc-design
by ZeroZ-labHigh-fidelity HTML design and prototype creation. Use this skill whenever the user asks to design, prototype, mock up, or build visual artifacts in HTML — including slide decks, interactive prototypes, landing pages, UI mockups, animations, or any visual design work. Also use when the user mentions Figma, design systems, UI kits, wireframes, presentations, or wants to explore visual design directions. Even if they just say "make it look good" or "design a screen for X", this skill applies.
build-backend-api-design
by ZeroZ-labAPI 和接口设计——稳定合约、清晰边界。当需要设计 REST/HTTP API、endpoint、接口契约、请求响应 DTO、错误语义、分页、幂等、权限边界或 API 合约测试时使用
build-backend-database
by ZeroZ-lab数据库工程——schema 设计、迁移安全、查询优化、数据完整性。用于新增/修改表结构、编写迁移、设计索引、优化慢查询、处理数据约束或数据修复计划
build-backend-service-patterns
by ZeroZ-lab服务架构模式——分层、通信、韧性。当需要设计后端服务架构、跨服务通信或处理分布式问题，或提到"微服务""重试""熔断"
build-cognitive-context
by ZeroZ-lab上下文工程——最大化 agent 输出质量。当开始新任务、上下文混乱或 agent 输出质量下降，或提到"上下文""context window""注意力"
build-cognitive-decision-record
by ZeroZ-lab架构决策记录（ADR）。当面临技术选型、架构决策、方案取舍需要记录，或提到"ADR""决策记录""为什么这样做"
build-cognitive-execution-engine
by ZeroZ-lab任务执行引擎——选择正确的执行模式。当 plan 已批准需要写代码，或提到"执行""实现""编码"
build-cognitive-source-driven
by ZeroZ-lab源码驱动开发——每个框架决策由官方文档背书。当使用不熟悉的 API、引入新依赖或不确定方法签名，或提到"文档""官方""API reference"
build-content-layout
by ZeroZ-lab信息设计与版式方法。适用于 artifact_type 为 document/deck/visual，当产物需要信息层级、阅读路径、构图、媒介适配，或提到"版式""排版""信息架构"
build-content-writing
by ZeroZ-lab内容架构与编辑方法。适用于 artifact_type 为 document/article/deck，当产物需要受众、主张、结构、证据、语气设计，或提到"写作""内容创作""文案"
build-frontend-browser-testing
by ZeroZ-lab浏览器测试验证——在真实浏览器中验证前端行为。当前端变更需要运行时验证、UI bug 调查或截图对照，或提到"浏览器测试""E2E""Playwright"
build-frontend-ui-engineering
by ZeroZ-lab前端 UI 工程——构建可访问、视觉精良的用户界面。当需要构建或修改 UI 组件、页面布局，或提到"组件""页面""前端""UI"
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.