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...
vben-admin
by m19803261706Vben Admin 5.x 前端开发规范。当创建页面、组件、API 接口或前端功能时自动使用。
cx-exec
by m19803261706Codex 侧 CX 任务执行。先做 worktree 检查，再 claim lease，然后实现、测试并更新共享状态。
cx-fix
by m19803261706Codex 侧 CX 缺陷修复。调查、定位、修复、测试，并在需要时遵守共享 lease 与 handoff 规则。
cx-plan
by m19803261706Codex 侧 CX 任务规划。读取 PRD/Design，生成任务清单、任务文档与共享状态。
cx-summary
by m19803261706Codex 侧 CX 汇总闭环。汇总 feature 结果、更新共享状态，并保持 docs 元信息一致。
cx-adr
by m19803261706CX 工作流 — 架构决策记录。当用户提到"架构决策"、"技术选型"、 "ADR"、"为什么选择 X"、"架构方案对比"时可能触发（通常由 cx-design 自动调用）。 仅在 L 规模或重大架构取舍出现时记录，保存到本地 开发文档/CX工作流/功能/{feature_title}/架构决策.md。
cx-fix
by m19803261706CX 工作流 — Bug 修复。当用户提到"修 bug"、"fix"、"报错"、"debug"、 "修复"时触发。默认走快速修复路径，复杂问题再升级为更深入的调查。
cx-prd
by m19803261706CX 工作流 — 需求收集与规模评估。当用户提到"新功能"、"需求"、"PRD"、 "我想做一个"、"帮我规划"、"收集需求"、"功能规划"时触发。 多轮对话收集需求，自动评估规模，保存到本地 开发文档/CX工作流/功能/{feature_title}/需求.md，并自动判断是否需要 Design。
cx-exec
by m19803261706CX 工作流 — 任务执行与自动推进。当用户提到"执行任务"、"开始开发"、 "实现功能"、"写代码"、"继续做"、"下一个任务"时触发。 默认读取项目级状态并自动推进可执行任务，完成后再进入 summary 闭环。
cx-help
by m19803261706Codex 侧 CX 工作流帮助。解释共享 cx core、可用 skills、lease/handoff/worktree 规则，以及下一步推荐动作。
cx-init
by m19803261706CX 工作流 — 项目初始化。每个项目都单独确认 developer_id、GitHub 同步策略、 agent teams、code review、worktree isolation、auto memory，并建立项目级 `开发文档/CX工作流 + .cx` 运行时真相目录。仅在用户明确调用 `/cx:cx-init` 时执行。
cx-config
by m19803261706CX 工作流 — 配置管理。查看或修改项目级 `开发文档/CX工作流/配置.json` 中公开的少量字段。仅在用户明确调用 `/cx:cx-config` 时执行。
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.