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.
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jeecg-desform
by jeecgbootJeecgBoot 表单设计器（desform）全生命周期管理——通过对话的方式创建、更新、复制、删除表单设计器，管理表单数据（CRUD），生成 PC/移动端表单视图，以及一键创建 OA 审批应用（表单+流程+授权）。只要用户意图涉及「表单设计器」就必须使用本技能，包括但不限于：创建或生成表单（"做一个请假表单"、"AI设计表单"、"desform"）、修改已有表单或字段（"加个字段"、"改一下表单"）、复制/删除表单、录入或查询表单数据、创建表单视图（含移动端）、以及咨询设计器功能（控件类型、字典配置、校验规则、关联记录、子表、公式计算、JS/CSS增强、默认值、外链等）。当用户想要创建带审批流程的 OA 应用时也应触发（"创建OA应用"、"创建审批单"、"创建报销单"、"创建请假单"、"做一个OA表单带流程"、"一键创建表单和流程"、"面试申请"、"出差申请"等）。即使用户只是描述字段需求（如"需要姓名、手机号字段"）而未明确说"表单"，只要语境指向表单设计器也应触发。注意：本技能仅处理表单设计器（desform），不处理 Online 表单——如果用户明确提到"Online表单"或"online表"，应使用 jeecg-onlform 技能。
jeecg-uniapp-codegen
by jeecgbootJeecgBoot UniApp3 移动端 CRUD 代码生成器。Use when user asks to generate UniApp3/mobile/APP CRUD pages, create mobile list/form pages, or says '生成移动端代码', '生成APP页面', 'uniapp代码生成', '移动端CRUD', '生成uniapp页面', '手机端页面', '移动端模块', 'generate uniapp code', 'mobile CRUD', 'uniapp page', 'APP端代码', '生成小程序页面', '生成H5页面'. Also triggers when user mentions generating mobile pages for an existing backend module, or wants to add mobile support to JeecgBoot modules. Even if user just says '给XX模块加个移动端页面' or '做个手机端的XX功能', this skill should trigger.
jeecg-codegen
by jeecgbootUse when user asks to generate JeecgBoot CRUD code, create a new module, add/modify fields on existing module, or says "代码生成", "生成代码", "创建模块", "新增功能", "建表", "加字段", "加一个字段", "增加字段", "新增字段", "修改字段", "删除字段", "generate code", "new entity", "add field"
jeecg-codegen-new
by jeecgbootUse when user asks to generate JeecgBoot CRUD code, create a new module, add/modify fields on existing module, or says "代码生成", "生成代码", "创建模块", "新增功能", "建表", "加字段", "加一个字段", "增加字段", "新增字段", "修改字段", "删除字段", "generate code", "new entity", "add field"
jeecg-bpmn
by jeecgbootUse when user asks to create/generate/edit/modify a BPM workflow, design a Flowable BPMN process, or says "创建流程", "生成流程", "新建流程", "设计流程", "画流程", "审批流程", "工作流", "BPM", "BPMN", "create flow", "create process", "new workflow", "generate workflow". Also triggers when user describes an approval chain like "先经理审批再HR审批" or mentions process nodes like "开始→审批→网关→结束". Also triggers for OA application creation: "创建OA应用", "创建审批单", "创建报销单", "创建请假单", "做一个OA表单带流程", "一键创建表单和流程", "create OA app", "create approval form with workflow". Also triggers for ANY operation on existing processes: "编辑流程", "修改流程", "删除监听器", "添加监听器", "删除节点", "添加节点", "修改节点", "配置节点", "流程中的", "edit process", "modify process", "delete listener", "add listener", "remove listener", "add node", "delete node", "configure node". Key rule: whenever user mentions a specific process name (like "网关测试") with any modification intent, this skill MUST be invoked FIRST before any manual API exploration.
jeecg-aiflow
by jeecgbootJeecgBoot AI 编排流程（AIFlow）全生命周期管理——通过自然语言描述需求，自动创建、编辑、查询、删除、调试、发布 AI 编排流程。 只要用户意图涉及「AI编排」「AIFlow」就必须使用本技能，包括但不限于： 创建 AI 编排流程（"做一个AI流程"、"创建aiflow"、"新建编排"、"做一个知识库问答流程"、"创建一个大模型对话流程"）， 修改已有流程（"给流程加个节点"、"改一下LLM的提示词"、"修改流程"）， 查询流程（"查看流程列表"、"有哪些AI流程"）， 删除流程（"删除流程"、"移除XX流程"）， 调试运行流程（"调试流程"、"运行流程"、"测试流程"）， 发布管理（"发布流程"、"取消发布"）， 复制流程（"复制流程"、"克隆流程"）。 关键词触发：aiflow、ai-flow、AI编排、AI流程、编排流程、大模型流程、知识库流程、LLM流程。 注意：本技能仅处理 AI 编排流程（AIFlow），不处理 BPMN 工作流（使用 jeecg-bpmn）、 不处理简流（使用 jeecg-lowcode-miniflow）。
jeecg-onlreport
by jeecgbootUse when user asks to create/edit/query Online reports, SQL reports, data reports, or says "创建报表", "生成报表", "新建报表", "查询报表", "online报表", "SQL报表", "数据报表", "统计报表", "create report", "generate report", "data report". Also triggers when user describes report requirements like "做一个销售统计报表", mentions JeecgBoot cgreport/online report, or says "查看现有报表" / "列出所有报表". This skill handles Online 报表 (SQL-driven data display/reports), not Online forms (cgform) or designer forms (desform).
jeecg-onlform
by jeecgbootJeecgBoot Online表单（cgform）全生命周期管理——通过API自动创建/编辑数据库表和表单配置， 支持单表、主子表、树表，26种控件类型，以及JS/Java/SQL增强、权限配置、数据CRUD、积木报表集成。 只要用户意图涉及「Online表单」就必须使用本技能，包括但不限于： 创建或配置数据库表（"建一张请假表"、"创建online表"、"做一个带下拉选择的表"、"低代码表单"、"在线表单"、"配置表"）、 修改已有Online表字段（"加个字段"、"改字段类型"、"加子表"、"删除字段"）、 配置表单增强（"JS增强"、"自定义按钮"、"表单联动"、"Java增强"、"SQL增强"）、 配置权限（"字段权限"、"按钮权限"、"数据权限"、"授权给角色"）、 管理表单数据（"插入数据"、"查询记录"、"导出CSV"、"造测试数据"）、 以及关联积木报表（"给这个表加报表"、"集成打印"）。 即使用户只描述了业务需求而没说"online"（如"做一个员工信息管理功能，包含姓名、部门下拉、入职日期"）， 只要涉及元数据驱动的表单配置，也应触发本技能。 注意：不要与「设计器表单」(desform)混淆——desform是拖拽式表单设计器，用skill jeecg-desform处理； 也不要与「Online报表」(cgreport)或「Online图表」(onlchart)混淆——它们是SQL驱动的只读展示。
jeecg-onlchart
by jeecgbootUse when user asks to create/edit Online graph charts, data visualization, or says "创建图表", "生成图表", "新建图表", "做一个图表", "online图表", "数据图表", "柱状图", "折线图", "饼图", "统计图", "可视化", "chart", "graph", "create chart", "generate chart", "bar chart", "line chart", "pie chart". Also triggers when user describes chart requirements like "做一个销售柱状图" or mentions data visualization like "用图表展示男女比例".
jeecg-dev
by jeecgbootJeecgBoot 开发规范（仅手动触发）。⚠️ 本技能只在用户显式输入 /jeecg-dev 命令时使用，禁止自动触发——编写/修改 JeecgBoot 代码、应用 GitHub PR/issue 改动、修复 bug、新增功能、重构、代码生成等场景都不要自动调用本技能。内容涵盖 update-begin/end 痕迹注释、命名规范、实体/控制器/服务模式、API 约定、建表规则与修改日志实践。MANUAL ONLY: invoke ONLY when the user explicitly runs the /jeecg-dev command. Do NOT auto-trigger on any code editing, bug fix, PR/issue application, refactoring, or code generation.
jeecg-system
by jeecgbootJeecgBoot 系统主数据查询与管理。Use when user asks to query/create/manage system master data, or says "查询角色", "查询用户", "查询部门", "查询字典", "创建字典", "创建角色", "查岗位", "查职务", "查租户", "查数据源", "查定时任务", "系统主数据", "query roles", "query users", "query depts", "create dict", "create role", "master data". Also triggers when other skills (desform, bpmn, codegen) need to look up or create roles, users, departments, dictionaries, or positions as dependencies — follows "先查后建" (query-first-create-later) principle.
jimubi-bigscreen
by jeecgbootUse when user asks to create/design a big screen (大屏), full-screen data visualization, or says "创建大屏", "生成大屏", "新建大屏", "设计大屏", "做一个大屏", "BI大屏", "数据大屏", "可视化大屏", "监控大屏", "create big screen", "design big screen", "BI visualization big screen". Also triggers when user describes big screen requirements like "做一个销售数据大屏" or mentions full-screen display like "展厅展示", "监控室大屏". Make sure to use this skill for big screens (大屏) — NOT dashboards (仪表盘/看板), which use a completely different layout and styling system.
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.