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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business-knowledge-workflow
by TencentBlueKing获取陌生业务知识并沉淀为 BK-CI skill 或架构文档时使用，例如阅读 iWiki、结合代码交叉验证、提炼模块边界和重写知识文档。当用户要先理解业务再写文档时优先使用。
backend-microservice-development
by TencentBlueKing编写 BK-CI 后端微服务代码时使用，例如新增 Resource、组织 API/Service/DAO 分层、依赖注入、服务归属判断和 Spring Boot 开发约定。当用户要做 Kotlin/Java 后端开发时优先使用。
frontend-vue-development
by TencentBlueKing编写 BK-CI 前端 Vue 页面和组件时使用，例如 Vue 组件开发、Vuex 状态管理、接口调用、样式约定和页面交互实现。当用户要改前端页面而不是后端服务时优先使用。
go-agent-development
by TencentBlueKing编写 BK-CI Go Agent 代码时使用，例如 Agent API 调用、任务处理、并发模式、错误处理、日志记录和宿主侧工具开发。当用户要写 Go 构建机侧代码而不是后端 Kotlin 服务时优先使用。
git-commit-specification
by TencentBlueKing编写 BK-CI Git 提交信息和整理提交边界时使用，例如选择 commit type、撰写 commit message、判断是否拆分提交和准备 PR 前自检。当用户要提交代码而不是讨论实现细节时优先使用。
unit-testing
by TencentBlueKing为 BK-CI 代码编写单元测试时使用，例如 JUnit5、MockK、测试组织、依赖 Mock、异常校验和 TDD 场景。当用户要补测试或用测试驱动实现时优先使用。
utility-components
by TencentBlueKing使用 BK-CI 中的具体工具组件时使用，例如 JWT、安全认证、表达式解析、线程池循环工具和责任链实现。当用户要直接复用这些组件而不是设计框架级实践时优先使用。
yaml-pipeline-transfer
by TencentBlueKing处理 BK-CI YAML 流水线导入导出、YAML 与 Model 双向转换、PAC、模板引用与 YAML 校验时使用。当用户提到 YAML 流水线、PAC、模板化、注释保留、表达式解析或转换调试时优先使用。
lightweight-ai-workflow
by TencentBlueKing处理 BK-CI AI 编码任务分流时使用，例如判断日常任务是否需要 OpenSpec、多 Agent、影响面清单或验证清单。
kotlin-backend-conventions
by TencentBlueKing编写 BK-CI Kotlin 后端代码时使用，例如 Kotlin 文件结构、格式、命名、函数设计、空安全和 BK-CI 项目内的调用约定。当用户要修改 `.kt` 或 `.kts` 后端代码并需要项目级 Kotlin 规范时优先使用。
worker-module-architecture
by TencentBlueKing处理 BK-CI Worker 执行器时使用，例如任务领取与执行、插件运行、日志上报、环境变量传递和制品归档。当用户要改 Worker 执行链路而不是插件定义或调度策略时优先使用。
repository-module-architecture
by TencentBlueKing处理 BK-CI 代码库接入、代码库授权、Webhook、SCM 集成、PAC 开关与仓库类型差异时使用。当用户提到 Git/SVN/GitHub/TGit、Webhook、仓库认证、PAC 代码库或提交记录时优先使用。
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.