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...
bp-component-design
by davidYichengWei提供组件级设计原则，包括类/模块设计、接口设计、数据模型、并发模型、错误处理。在系统设计阶段讨论组件详细设计时使用，或在 code review 中评估组件质量时使用。
bp-distributed-systems
by davidYichengWei提供分布式系统设计 best practices（对应常见 fallacies）及各 SDLC 阶段 checklist。当需求涉及网络通信、多节点协调、数据一致性、故障恢复时使用。
bp-performance-optimization
by davidYichengWei提供性能优化方法论、设计原则和具体优化规则。在编写代码、优化性能瓶颈、进行 code review 时使用，涵盖 CPU、内存、I/O、并发等维度。
bp-skill-authoring
by davidYichengWei指导编写和改进 Agent Skill 文件（SKILL.md），涵盖 YAML frontmatter、精简写作、渐进式披露、常用模式。当用户要创建新 Skill、改进现有 SKILL.md、或询问 Skill 编写规范时使用。
bp-coding-best-practices
by davidYichengWei通用编码最佳实践。在编写或 review 代码时使用。涵盖可读性、命名、函数设计、控制流、资源安全、注释规范。
bp-architecture-design
by davidYichengWei提供架构设计原则，包括模块划分、依赖管理、数据架构、接口设计。在系统设计阶段讨论方案概览时使用，或在 code review 中评估架构合理性时使用。
troubleshooting
by davidYichengWei问题排查。当用户遇到编译错误、运行时异常、单测失败、流水线报错、现网告警等需要定位问题时触发。
workflow-requirements-clarification
by davidYichengWei需求澄清。只负责明确"要解决什么问题"，生成 spec.md 的前三章节（背景、目标、需求）。禁止在本阶段讨论设计方案——设计是 workflow-system-design skill 的职责。
std-cpp
by davidYichengWei提供 C++ 编码规范（基于 Google C++ Style Guide）。当编写或 review C++ 代码（.cc/.cpp/.h 文件）时使用。
std-go
by davidYichengWei提供 Go 编码规范（基于 Google Go Style Guide）。当编写或 review Go 代码（.go 文件）时使用。
workflow-system-design
by davidYichengWei系统设计。当 spec.md 前三章节（背景、目标、需求）已完整但设计章节为空时调用。按 spec.md 章节顺序逐个与用户讨论，每轮只处理一个 section。
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.