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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bid-verification
by youyouhe对招标文件分析报告进行逐项核实，与原始采购文件交叉验证每一个关键数据点。 检查分析报告中的金额、分值、资格条件、时间节点、评分规则等是否与原文一致， 识别幻觉数据、遗漏信息、数值错误。当用户要求核实/校验/审核分析报告时触发。
generate-placeholder-toolkit
by youyouhe为生成的Word投标文件创建占位符替换工具包。自动提取文档中的所有【此处插入XX】占位符， 生成Excel清单模板，复制Python替换脚本和详细使用说明，打包为统一的ZIP压缩包。 当用户要求"生成工具包"、"创建占位符工具"、"打包替换工具"时触发。 前置条件：响应文件/ 目录下已有生成完成的 Word 文档（.docx）。
bid-analysis
by youyouhe分析政府采购招标/磋商文件（PDF、Word、Excel），提取评分标准、技术需求、商务要求、资格条件、预算等关键信息， 生成结构化大纲和响应文件目录。支持多文件输入（招标公告+技术规范表+合同模板等）。 适用于竞争性磋商、公开招标、邀请招标等场景。 当用户提供招标文件/磋商文件/采购文件并要求分析、理解需求、提取评分标准、生成投标大纲时触发。
bid-commercial-proposal
by youyouhe编写投标/响应文件的商务标部分。从分析报告中动态读取附件清单、商务条件、 资格要求和评分标准，收集公司信息后逐附件编写报价函、资格证明、业绩材料、 声明函等商务文件。输出Markdown格式文件到 响应文件/ 目录。 当用户要求编写商务标、商务文件、投标附件、报价文件时触发。 也支持修复模式：当用户要求修复/补充商务文件、处理质检反馈时触发。 前置条件：需已完成 bid-analysis 生成分析报告。
bid-material-search
by youyouhe基于已提取的投标资料图片和index.json索引，构建FastAPI检索服务， 支持关键词搜索、分类过滤、文档类型查询，提供图片静态文件服务， 并支持自动替换响应文件中的占位符为实际图片引用。 当用户需要查询投标资料（营业执照、证书、合同、业绩等）、 启动资料检索服务、管理索引条目（增删改）、 或替换响应文件中的【此处插入XX扫描件】占位符时触发。 前置条件：需已通过 bid-material-extraction 完成图片提取和索引建立。
bid-assembly
by youyouhe对已编写的投标/响应文件进行全面质检，核对完整性、一致性和合规性。 从分析报告出发独立审查所有输出文件，检测遗漏、矛盾、占位符残留等问题， 生成核对报告、最终目录和装订指南。 当用户要求检查标书、核对投标文件、质检、汇总、组装最终文件时触发。 前置条件：需已完成 bid-tech-proposal 和/或 bid-commercial-proposal 的编写。
bid-commercial-proposal
by youyouhe编写投标/响应文件的商务标部分。从分析报告中动态读取附件清单、商务条件、 资格要求和评分标准，收集公司信息后逐附件编写报价函、资格证明、业绩材料、 声明函等商务文件。输出Markdown格式文件到 响应文件/ 目录。 当用户要求编写商务标、商务文件、投标附件、报价文件时触发。 也支持修复模式：当用户要求修复/补充商务文件、处理质检反馈时触发。 前置条件：需已完成 bid-analysis 生成分析报告。
bid-manager
by youyouhe投标全流程管理器。编排所有 bid skills 按流水线自动执行，支持一键投标、 断点续跑、指定阶段启动。10个阶段：分析→核实→信息收集→商务标→技术标→ 图表→扫描件→质检→自动修复→生成Word。 当用户要求一键投标、全流程投标、管理投标进度、继续投标流程时触发。 前置条件：需要有招标/磋商/采购文件。
bid-tech-proposal
by youyouhe编写投标/响应文件的技术标部分。从分析报告中动态读取评分标准和技术需求， 按评分维度逆向设计章节结构，逐文件编写技术方案、技术服务响应表、培训方案等。 输出Markdown格式文件到 响应文件/ 目录。 当用户要求编写技术标、技术方案、技术响应文件时触发。 也支持修复模式：当用户要求修复/补充技术文件、处理质检反馈时触发。 前置条件：需已完成 bid-analysis 生成分析报告。
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.