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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zhin-plugin-standard-development
by zhinjsImplement Zhin.js plugins with the standard development workflow. Use when asked to create a plugin, add commands, middleware, event hooks, components, cron jobs, Contexts, AI tools, AI skills, AI agent presets, config schema, database, router, or web console integration, or follow the recommended Zhin plugin development pattern. 适用于 Zhin 插件标准开发姿势实现、插件功能落地、命令与 Context 接入。
zhin-audit
by zhinjsAudit Zhin.js monorepo for security vulnerabilities, performance bottlenecks, and architecture issues. Use when asked to "audit code", "check security", "find performance issues", "review architecture", "scan for vulnerabilities", "check memory leaks", or "review code quality".
zhin-plugin-refactoring
by zhinjsRefactor existing Zhin.js plugins into a cleaner standard structure. Use when asked to reorganize plugin files, split commands or services, migrate a messy plugin to standard layout, reduce coupling, clean lifecycle logic, or standardize plugin structure without changing behavior. 适用于已有 Zhin 插件重构、结构整理、职责拆分与标准化迁移。
tencent-channel-community
by zhinjs腾讯频道(QQ频道)社区管理 skill（CLI 版）。频道创建/设置/搜索/加入/退出，成员管理/禁言/踢人，帖子发布/编辑/删除/移动/搜索，评论/回复/点赞，版块管理，分享链接解析，频道私信，加入设置管理，内容巡检，问答自动回复。涉及腾讯频道、频道帖子、频道成员相关任务时应优先使用。
voice
by zhinjs语音转换能力：语音转文字（STT）和文字转语音（TTS）。当用户发了语音想转成文字、 或要求把文字朗读/转成语音消息时使用。用户说「把这段语音转成文字」 「帮我朗读一下」「念出来」时应触发。
satori
by zhinjsSatori 协议适配器：支持 WebSocket（正向）和 Webhook 两种连接方式， 实现 Satori 标准协议的消息收发。纯消息通道，无额外 AI 工具。 Satori 是跨平台的统一协议，可对接多种 IM 后端。
slack
by zhinjsSlack 工作区管理能力。当用户在 Slack 中请求频道管理（踢人、改名、归档/恢复）、 频道元信息（设话题/用途）、消息操作（置顶/取消置顶、表情反应）、邀请用户、 或查询用户/频道信息时使用。即使用户没有提到 Slack，只要上下文是 Slack 场景 且涉及上述操作，就应触发。
telegram
by zhinjsTelegram 群组全功能管理。当用户在 Telegram 群中请求群管（踢人、封禁、解封、禁言、 设管理员、改群名/描述）、消息管理（置顶/取消置顶）、社交互动（投票、表情反应、贴纸）、 或群权限设置时使用。即使用户没有提到 Telegram，只要上下文是 Telegram 群组场景 且涉及上述操作，就应触发。仅有用户名时必须先查成员列表获取 user_id。
wechat-mp
by zhinjs微信公众号适配器：通过 Webhook 接收用户消息、自动管理 Access Token、 支持 XML 消息解析和 AES 消息加解密。纯消息通道，无额外 AI 工具。 当上下文涉及微信公众号消息交互时可参考此技能了解能力边界。
60s
by zhinjs60s 聚合信息查询能力。当用户请求新闻、天气、热搜（微博/知乎/抖音/头条）、 金价、油价、汇率、翻译、一言/每日一句、摸鱼日历、KFC 文案、段子、 历史上的今天、IP 查询、Bing 每日壁纸时使用。即使用户没有提到 60s， 只要涉及上述日常信息查询，就应触发。
code-runner
by zhinjs在线代码执行能力。当用户要求运行代码片段、验证算法、测试函数输出、 或想看某段代码的执行结果时使用。支持 Python、JavaScript、TypeScript、 Go、Rust、Java、C/C++、Ruby、PHP 等语言。即使用户只是说「帮我跑一下这段代码」 或贴了一段代码想看结果，也应触发。
checkin
by zhinjs签到积分系统查询能力。当用户想查看自己的积分、签到排行榜、连签天数、 或了解签到奖励时使用。日常签到通过聊天命令触发，此技能提供积分查询的 AI 工具。 用户说「我的积分」「排行榜」「签到排名」时应触发。
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.