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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page-codegen
by ChenyCHENYUUse when: generating Vue 3 page code from either a page-spec JSON or a natural language description. Outputs index.vue + data.ts + index.scss following Robot Admin conventions. Triggers on: page generation, code generation, 生成页面, 代码生成, vue页面, codegen, 页面骨架, scaffold, 建个页面, 写个页面, 帮我做个页面, 口述需求, natural language page request.
prototype-scan
by ChenyCHENYUUse when: analyzing Axure exported HTML prototype files or detailed design documents to extract page inventory, classify interaction patterns, and produce a structured page-spec JSON for Vue 3 + Naive UI development. Triggers on: prototype analysis, axure scan, page inventory, 原型解析, 页面清单, axure转vue, 详设文档, design doc.
route-sync
by ChenyCHENYUUse when: registering new pages into the dynamic router system. Adds route entries to dynamicRouter.json and optionally updates keepAliveConfig.ts. Triggers on: route registration, 注册路由, 添加菜单, menu registration, 路由配置, dynamicRouter, 新增页面路由.
api-contract
by ChenyCHENYUUse when: generating TypeScript API layer (type definitions + request functions) from page-spec JSON or Swagger/OpenAPI docs. Triggers on: api contract, api generation, 接口约定, 生成api, swagger to ts, openapi, 接口文件, api层.
branch-sync
by ChenyCHENYUUse when: upgrading dependencies, adding common features, or when the user explicitly requests branch sync check. Triggers on: 分支同步, branch sync, 版本升级, dependency upgrade, 同步更新, sync branches, 依赖更新, 通用功能.
convention-audit
by ChenyCHENYUUse when: reviewing code for compliance with Robot Admin project conventions, or when generating a compliance report. Triggers on: convention check, 规范检查, audit, 代码审查, code review, 命名规范, naming convention, 代码规范.
mock-codegen
by ChenyCHENYUUse when: generating mock data for development without a backend. Optional skill - activated when user confirms or explicitly requests it. Triggers on: 生成mock, mock数据, 模拟数据, mock生成, 前端mock, 联调前mock.
code-fix
by ChenyCHENYUUse when: fixing code convention issues found in convention-audit reports. Triggers on: 自动修复, 整改偏差, 修复报告, 规范整改, 修复偏差, code fix, 整改规范.
dict-sync
by ChenyCHENYUUse when: syncing data dictionary entries to the backend, pulling the current online dictionary baseline, or checking which dictionaries used in data.ts are missing from the system. Triggers on: 同步字典, 创建字典, 刷新字典基线, 字典对比, 字典审计, dict sync, create dict.
api-contract
by ChenyCHENYUUse when: designing API contracts between frontend and backend based on prototype page inventory, defining request/response field structures, establishing naming conventions, and generating an api.md file per page for team alignment before coding. Triggers on: api contract, interface design, 接口约定, 前后端对齐, 字段定义, 接口设计, api.md.
permission-sync
by ChenyCHENYUUse when: managing roles, authorizing menus to roles, attaching action buttons (type=A) to page menus, or wiring permission field in data.ts toolbar config. Triggers on: 创建角色, 角色管理, 角色授权, 给角色分配菜单, 挂动作, 添加动作按钮, 同步权限, 权限码注册, role assign, permission sync.
menu-sync
by ChenyCHENYUUse when: creating system menus for newly generated pages, batch registering menus, or syncing pages.ts entries to the backend menu table. Triggers on: 创建菜单, 注册菜单, 同步菜单, 补菜单, menu sync, create menu, register menu.
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.