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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gscore-plugin-development
by Genshin-bots当用户要求"帮我写一个 GsCore 插件"、"给这个插件加功能"、"改造触发器支持 AI"、 "怎么用 to_ai"、"注册 ai_tools"、"写一个游戏查询插件"、"插件帮助怎么注册"、 "能力代理/代理画像"、"怎么为触发器添加AI功能"、"几个触发器的差别在哪"、"数据库和配置项怎么添加" "如何把数据库表挂到网页控制台"、"PIL/htmlkit/playwright 哪个用哪个"、 "插件怎么挂自己的 HTTP 接口"、"插件怎么注册 FastAPI 路由"、 "怎么扩展 RAG 嵌入后端"、"注册自定义 Embedding Provider"时触发此 SKILL。 对所有 GsCore Bot 插件开发任务都应优先读取此 SKILL。 为 GsCore 机器人框架编写插件的完整指南。涵盖项目级目录规范（参照 ZZZeroUID / SayuStock）、 Plugins/SV 双层架构、各类触发器的语义差异（on_command vs on_prefix vs on_fullmatch vs on_keyword vs on_regex vs on_message vs on_file）、on_meta 监听平台元事件（进群 / 退群 / 戳一戳 三种标准事件）、消息收发与撤回（wait_recall / unsend）/ 禁言（ban）、数据库操作并注册到网页控制台 （site.register_admin / GsAdminModel）、订阅系统（gs_subscribe）、定时任务、配置管理、 帮助系统（register_help + get_new_help）、推荐的渲染范式（优先 PIL → htmlkit → playwright 兜底）、AI 工具集成（@ai_tools、to_ai、ai_return、create_agent）、 知识库 / 别名注册、启动钩子、to_ai 批量改造工作流、为插件挂 FastAPI 后端接口、 嵌入 Provider 注册表（插件扩展 RAG 嵌入后端）。
gscore-ai-core-api
by Genshin-bots当用户要求"AI Core 给插件提供了哪些 API"、"@ai_tools 装饰器怎么用"、 "category='self'/'buildin'/'common'/'media'/'default' 有什么区别"、 "怎么把已有触发器改造成 AI 工具 to_ai / MockBot / ai_return 怎么用"、 "create_agent 怎么创建临时 Agent"、"ai_entity / ai_alias / ai_image 怎么注册"、 "Persona / Memory / Scheduled Task / MCP / 嵌入 Provider 怎么调"、 "ToolContext / ToolBase / KnowledgePoint / ImageEntity 是什么类型"、 "buildin 都有哪些内置工具"、"get_registered_tools / get_all_tools 怎么查"、 "send_meme / collect_meme / understand_image / web_search 怎么用"时触发此 SKILL。 对所有「插件作者要对接 AI Core 提供的 API」的任务都应优先读取此 SKILL。 GsCore（早柚核心 / gsuid-core）`gsuid_core/ai_core/` 模块的完整 API 速查手册。 涵盖：模块导入速查（完整 import 块）、@ai_tools 装饰器签名与四种函数模式、 工具分类系统（self/buildin/common/media/default/mcp 五类 + 保底池架构图）、 触发器→AI 工具桥接（to_ai / MockBot / ai_return / send_message_by_ai 资源ID 机制）、 create_agent 临时 Agent 与 GsCoreAIAgent.run() 全部参数、知识库注册（ai_entity / add_manual_knowledge 手动知识管理）、别名注册（含 C2 scope 变更）、 图片实体注册（ai_image）、内置工具大全（self/buildin/common/media/default 几十个 工具签名 + Kanban 任务编排 + Capability Agent 能力代理）、Persona 角色系统、 Memory 记忆
gscore-adapter-development
by Genshin-bots当用户要求"帮我写一个 GsCore 适配器"、"把 XXX 平台接入早柚核心 / gsuid-core"、 "怎么连接 core 的 WebSocket"、"MessageReceive / MessageSend 怎么填"、 "上报消息怎么写"、"core 发回来的消息怎么解析"、"base64:// 和 link:// 有什么区别"、 "按钮 / Markdown 怎么适配到我的平台"、"node 合并转发怎么处理"、"双 ID 平台 group_id 怎么拼"、 "QQ 官方 msg_id / msg_seq 时序问题"、"为什么我的命令前缀被吞了 / 没被识别"、 "适配器 token 鉴权 / 断线重连怎么写"、"log_ 日志包是什么"、 "进退群 / 戳一戳元事件怎么上报"、"wait_recall 回执 / unsend 撤回 / ban 禁言怎么在适配器实现"时触发此 SKILL。 凡是"把某个聊天平台接入 GsCore"或"调试 core 与适配器之间通信"的任务都应优先读取此 SKILL。 为 GsCore（早柚核心 / gsuid-core）机器人框架编写**平台适配器**的完整指南。适配器是运行在 Bot 平台一侧、通过 WebSocket 与 core 通信的连接器（区别于运行在 core 内部的"插件"）。 涵盖：早柚协议总览与二进制帧、三类数据结构（Message / MessageReceive / MessageSend / Button）、 bot_id 的三层语义（路由 ID / 平台 ID / bot_self_id）、连接生命周期（token 鉴权 / 心跳 / 断线重连 / 收发双协程骨架）、上报消息（平台→core，每种 content 类型如何构造、user_pm 映射、is_tome 机制、 命令前缀处理）、发送消息（core→平台，recv 循环按 bot_id 路由、每种 type 的落地处理）、 base64:// 与 link:// 双形态图片处理、按钮与 Markdown 跨平台映射、node 合并转发、双 ID 平台 （villa / heybox）、QQ 官方时序、回调按钮上报、log_ 日志包、元事件上报（user_join_group / user_exit_group / poke 三种标准事件）、echo 撤回回执、撤回 / 禁言控制包、
gscore-deploy
by Genshin-bots当用户要求"部署 GsCore / gsuid_core"、"搭建早柚核心"、"把 Core 跑起来"、"Core 启动失败 / 报错"、"Core 配置 WebConsole / 网页控制台"、"连接 NoneBot2 / AstrBot / Koishi 等 Bot"、"安装 / 更新 / 卸载插件"、"切换 Git 镜像源"、"用 Docker 部署"、"挂载模式 / Bundle 模式"、"MySQL 切换"、"配置 WS_TOKEN / TRUSTED_IPS"、"公网部署"、"配置 AI 核心 启用 OpenAI 兼容 API / Tavily / 嵌入模型"、"升级 v3 到 v4"、"core 启动报错排查" "Docker 内 git 代理配置"、"WebConsole 注册码忘记"、"Core 与 Bot 不在同一台机器上" "HTTPS 与 WebConsole 加密握手" 时触发此 SKILL。 GsCore（gsuid_core）项目面向部署者的完整指南：覆盖环境与依赖检查、四种 Python 包管理器（uv / poetry / pdm / pip）的安装与启动、Docker 两种部署模式 （mount / bundle）、配置文件体系（config.json / core_config.json / ai_config.json / openai_config.json / tavily_config.json）、WebConsole 体系（含注册码 / 加密握手 / 限流）、WebSocket 安全（WS_TOKEN / TRUSTED_IPS / 失败封禁 / IP 限流）、插件管理体系（命令安装 / 手动安装 / Git 镜像源 / 自动更新）、Bot 适配器连接清单（NoneBot2 / Hoshino / AstrBot / ZeroBot / YunZai / Koishi / XYBotV2 / napcat / gs-core-adapter）、 数据库配置（SQLite 默认 / MySQL / PostgreSQL / 自定义 URL）、AI 核心部署 关键开关与外部服务（OpenAI 兼容 API / 嵌入 / Rerank / Qdrant / Tavily / Exa / MCP）、从 GenshinUID v3 迁移、故障排查清单、目录与路径速查。
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.