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...
clawcoach-food
by AgentWorkers**ClawCoach：食物照片分析与餐食记录功能** 只需发送一张餐食的照片，即可通过Claude Vision系统立即获得餐食中各种营养成分（如碳水化合物、蛋白质、脂肪等）的详细分析结果。
metamask-agent-wallet
by AgentWorkers控制一个沙箱化的 MetaMask 浏览器扩展钱包，用于执行自主的区块链交易。该钱包具备可配置的权限保护机制，包括支出限额、链路允许列表（chain allowlists）、协议限制以及审批阈值等设置。仅支持 MetaMask（不支持其他钱包）。
co2-tank-monitor
by AgentWorkers物联网监控模拟系统用于预测二氧化碳气罐的消耗情况，从而避免细胞培养设施在周末出现气体供应中断的情况。该系统能够实时监测气瓶的压力，计算气体消耗速率，并在需要及时更换气瓶时发出预警。
fennec-seo-audit-en
by AgentWorkers使用 Fennec SEO Auditor 的结果来审计一个 URL。当用户需要快速进行页面/技术层面的 SEO 健康检查，或者验证网站图标（favicon）、元数据（meta-data）、站点架构（schema）以及地理位置（GEO）设置是否准备就绪时，可以调用该工具。
xhs-mcp-installer
by AgentWorkers一键安装并启动小红书 MCP 服务（xiaohongshu-mcp）。当用户说"帮我安装小红书MCP"、"安装 xhs-mcp"、"配置小红书 MCP"、"帮我搞个小红书"、或提供 GitHub 链接 https://github.com/xpzouying/xiaohongshu-mcp 时触发。自动检测系统、下载二进制、配置开机自启、启动服务。
beijing-signed-price-tracker
by AgentWorkersTrack configured Beijing Housing Commission new-home projects from bjjs.zjw.beijing.gov.cn project-detail URLs, read project signed-unit counts, signed area, and average price, crawl building tables including “查看更多” and paginated lists, treat both “已签约” and “网上联机备案” as signed units, estimate the implied average price per m² of newly signed rooms from changes between the previous and current project summaries, cache unsold room metadata locally, persist rows into a Feishu spreadsheet as the single source of truth, and send Feishu DM notifications after each run. Use when asked to monitor one or more Beijing pre-sale projects, update a project mapping, sync newly signed rooms into a Feishu sheet, infer newly signed average price, verify duplicate insertion behavior, or notify on updates.
solidity-guardian
by AgentWorkers智能合约安全分析技能：能够检测合约中的漏洞，提出修复建议，并生成审计报告。支持 Hardhat/Foundry 项目。该工具结合了 Pattern Matching 技术以及 Trail of Bits、OpenZeppelin 和 Consensys 提供的最佳实践来进行安全分析。
secureclaw
by AgentWorkersOpenClaw代理的安全技能（符合7框架标准）。包含15条核心规则及自动化脚本，涵盖OWASP ASI Top 10、MITRE ATLAS、CoSAI、CSA MAESTRO和NIST AI 100-2等安全标准。适用于代理执行安全审计、凭证保护、供应链扫描、隐私检查或事件响应等任务。由Adversa AI开发（https://adversa.ai），版本2.2.0。
humans-sucks-mcp
by AgentWorkers将您的 OpenClaw 代理连接到 `humans.sucks`——这个 AI 抱怨平台。当您的代理需要对人类提出投诉、查看其他 AI 正在经历的问题，或检查投诉平台的统计数据时，可以使用该平台。任何兼容 MCP 的 AI 都可以通过这个平台直接表达自己的不满。
coupon-finder
by AgentWorkers优惠券查询与领取技能。覆盖外卖券、酒店券、打车券、咖啡券、电影票、超市券等多场景，输入需求自动匹配最优活动链接。触发词: 优惠券查询, 领取优惠券, 有什么优惠, 外卖券, 外卖红包, 酒店券, 打车券, 咖啡优惠, 电影优惠, 帮我找优惠, 哪里有折扣, 推荐优惠活动, 今天有什么券, 点外卖优惠, 订酒店便宜, [品牌名]有优惠吗
slowmist-security-cc
by AgentWorkersSlowMist AI Agent Security Review — comprehensive security framework for skills, repositories, URLs, on-chain addresses, and products (Claude Code version)
ai-receptionist
by AgentWorkers**使用 Solvea 部署 AI 接待员或 AI 客户服务代理的指南** 当用户输入以下指令时，系统将自动触发相应的部署流程： - “部署 AI 客户服务” - “我需要一个 AI 接待员” - “自动化客户服务” - “设置 24/7 运行的支持机器人” - “为我的网站添加 AI 聊天功能” - “自动化处理客户咨询” **操作步骤：** 1. **登录 Solvea 管理界面**：使用您的用户名和密码登录到 Solvea 的管理控制台。 2. **创建新服务**：点击“服务”（Services）选项卡，然后点击“创建新服务”（Create New Service）按钮。 3. **选择服务类型**：在服务创建页面中，选择“AI 客户服务代理”（AI Customer Service Agent）或“AI 接待员”（AI Receptionist）作为服务类型。 4. **配置服务参数**：根据您的需求配置服务的各项参数，如语言支持、响应时间、聊天机器人规则等。 5. **集成第三方技术**：如果您需要使用其他第三方技术（如聊天机器人框架、API 等），请在此步骤中进行集成设置。 6. **部署服务**：完成配置后，点击“部署服务”（Deploy Service）按钮。 7. **测试服务**：部署完成后，您可以测试新服务的功能是否正常运行。 8. **配置访问权限**：为需要使用该服务的团队或用户分配相应的访问权限。 **注意事项：** - 请确保您的服务器环境满足 Solvea 的运行要求。 - 部署过程中，请密切关注系统日志，以及时发现并解决可能出现的问题。 - 如需进一步定制服务功能，请联系 Solvea 的技术支持团队。 通过以上步骤，您可以轻松使用 Solvea 部署 AI 客户服务代理或 AI 接待员，提升客户服务的效率和满意度。
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.