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...
yy-frontend-style-bem-optimizer
by bulls-cows前端 BEM 命名规范转换与 CSS 样式优化器。将 HTML/JSX/TSX/Vue/Svelte/Astro 代码中的 class/className 属性 与对应的 CSS/Sass/Less/Stylus 选择器同步转换为 BEM 命名格式，将 CSS 属性按 csscomb zen 预设顺序排列， 将扁平 CSS 规则重组为 BEM 嵌套结构，并将样式规则按逻辑域拆分为独立集合。 触发此技能的场景包括但不限于： 用户要求将类名转换为 BEM 规范、重构旧项目样式命名、统一团队 CSS 命名规范； 用户要求整理 CSS 属性的书写顺序、CSS 属性排序； 用户要求将扁平的 CSS/SCSS 规则重组为嵌套结构、用 & 组织选择器； 用户要求将臃肿的样式文件按功能模块拆分； 用户的口语化表达如"这个 scss 太乱了帮我整理下"、"把 class 名改成规范格式"、"把 class 改规范"、 "CSS 属性顺序排一下"、"这个样式文件拆开吧"、"这个组件的 class 命名不规范帮我改改"、 "帮我把 scss 里面的选择器整理成 BEM 格式"、"CSS 属性写得太乱了帮我排一下序"。 不应触发：纯理论问答不涉及代码转换；非前端类名场景（JS/TS 变量名、后端命名、数据库字段）； 改样式值不改命名；要求用 CSS Modules、CSS-in-JS、Tailwind 等非 BEM 方案重写样式。
yy-resume
by bulls-cows创建 HTML 格式简历，支持多种职业类型模板，支持 A4 纸张打印。用于用户需要生成专业简历时。 不用于生成求职信、面试自我介绍、个人介绍页或公众号文章，也不用于审核或修改已有简历的措辞。
yy-create-python-script
by bulls-cows创建基于当前技能 `scripts/` 参考工程的 Python 脚本项目骨架，或按同一结构补齐空白/轻量脚本项目。 当用户需要快速落地命令行脚本、CSV/文件处理流水线、可选并发处理或 PyInstaller 打包脚本时触发，不用于 Web 服务、数据科学实验、单文件代码片段或成熟项目重构。
yy-create-readme
by bulls-cows创建或更新项目根目录下的 README.md 文件。自动分析项目结构， 生成专业、清晰的 README 文档，包含项目描述、技术栈、安装使用、特性等核心内容。
yy-create-report
by bulls-cows生成面向管理人员的业务视角工作报告。当用户说"生成工作报告"、"写工作报告"、"汇总工作"、"生成工作总结"时触发。
yy-create-rule
by bulls-cows创建或更新规则文档，并更新 AGENTS.md 中的引用关系。 用于：用户想要创建规则、记录最佳实践、记录 bug 修复经验、记录架构决策。
yy-create-skill
by bulls-cows创建或更新 Skill（技能）。用于创建新技能、更新现有技能或标准化工作流程，不用于创建规则文件或普通文件。
yy-create-template-project
by bulls-cows从用户提供的现有项目中提炼脱敏、可复用的模板项目，或优化完善已有模板项目。 当用户需要生成、整理、补齐或优化模板项目时触发，不用于直接创建业务项目、迁移成熟系统或复制含敏感数据的完整项目。
yy-create-vue3
by bulls-cows创建基于当前技能 `scripts/` 参考工程的 Vue 3 + TypeScript + Vite 项目骨架，或按同一结构补齐空白/轻量项目。 当用户需要快速落地统一目录、配置和页面骨架时触发，不用于复杂迁移、重构或通用 Vue 问答。
yy-design-ui
by bulls-cows创建符合国人审美的网页界面设计。当用户需要设计网页、组件、页面或应用界面时使用。 专注于简洁、精致、和谐的设计风格，生成可直接使用的前端代码。
yy-detect-terminal
by bulls-cows识别并记录本地终端命令能力。用于创建或更新项目根目录 TERMINAL.LOCAL.md，或在执行终端命令前确认 shell、命令可用性和搜索命令； 不用于执行 lint、测试、构建、部署等业务命令。
yy-distill
by bulls-cows从用户指定内容中提炼五维能力模型，或按既有能力模型要求重构指定内容，用于分析和指导 AI agent 的通用行为与底层能力。 当用户要求提炼能力、分析能力模型、提取思维模式，或要求按能力模型重写、改写、重构内容时触发。
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.