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...
xlsx
by LeastBit全面的电子表格创建、编辑和分析功能，支持公式、格式化、数据分析和可视化。当 Claude 需要处理电子表格（.xlsx、.xlsm、.csv、.tsv 等）时使用，包括：(1) 创建带有公式和格式的新电子表格，(2) 读取或分析数据，(3) 修改现有电子表格同时保留公式，(4) 电子表格中的数据分析和可视化，或 (5) 重新计算公式
algorithmic-art
by LeastBit使用 p5.js 创建带有种子随机性和交互式参数探索的算法艺术。当用户请求使用代码创建艺术、生成艺术、算法艺术、流场或粒子系统时使用此技能。创作原创算法艺术，而不是复制现有艺术家的作品，以避免版权侵犯。
brand-guidelines
by LeastBit将 Anthropic 官方品牌颜色和字体应用于任何可能需要 Anthropic 视觉风格的作品。适用于品牌颜色或样式指南、视觉格式化或公司设计标准的场景。
canvas-design
by LeastBit使用设计哲学创作精美的 .png 和 .pdf 格式视觉艺术作品。当用户要求创作海报、艺术品、设计作品或其他静态作品时，应使用此技能。创作原创视觉设计，切勿复制现有艺术家的作品以避免版权侵权。
doc-coauthoring
by LeastBit引导用户通过结构化的工作流程来协作撰写文档。当用户想要撰写文档、提案、技术规格说明、决策文档或类似的结构化内容时使用。这个工作流程帮助用户高效地传递上下文信息、通过迭代优化内容，并验证文档对读者是否有效。当用户提到撰写文档、创建提案、起草规格说明或类似的文档任务时触发。
docx
by LeastBit全面的文档创建、编辑和分析功能，支持修订追踪、批注、格式保留和文本提取。当 Claude 需要处理专业文档（.docx 文件）时使用：(1) 创建新文档，(2) 修改或编辑内容，(3) 处理修订追踪，(4) 添加批注，或其他任何文档任务
frontend-design
by LeastBit创建独特的、生产级别的高质量前端界面。当用户要求构建网页组件、页面、作品、海报或应用程序时使用此技能（例如网站、落地页、仪表盘、React 组件、HTML/CSS 布局，或对任何网页 UI 进行样式美化）。生成富有创意、精致的代码和 UI 设计，避免千篇一律的 AI 审美。
internal-comms
by LeastBit一套帮助我撰写各类内部沟通文档的资源，使用公司偏好的格式。当被要求撰写任何形式的内部沟通文档（状态报告、领导层更新、3P 更新、公司简报、常见问题解答、事件报告、项目更新等）时，Claude 应使用此技能。
mcp-builder
by LeastBit构建高质量 MCP（模型上下文协议）服务器的指南，使 LLM 能够通过精心设计的工具与外部服务交互。在使用 Python (FastMCP) 或 Node/TypeScript (MCP SDK) 构建 MCP 服务器以集成外部 API 或服务时使用。
全面的 PDF 操作工具包，用于提取文本和表格、创建新 PDF、合并/拆分文档以及处理表单。当 Claude 需要填写 PDF 表单或以编程方式大规模处理、生成或分析 PDF 文档时使用。
pptx
by LeastBit演示文稿创建、编辑和分析。当 Claude 需要处理演示文稿（.pptx 文件）时使用，包括：(1) 创建新演示文稿，(2) 修改或编辑内容，(3) 处理版式，(4) 添加批注或演讲者备注，或其他任何演示文稿任务
skill-creator
by LeastBit创建高效技能的指南。当用户想要创建新技能（或更新现有技能）以扩展 Claude 的能力，包括专业知识、工作流程或工具集成时，应使用此技能。
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.