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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yida-formula
by openyida宜搭表单公式编写规范，包含函数速查、语法规则、常见场景示例。不适用于：配置业务关联规则/高级函数（应使用 yida-business-rule），或创建表单字段结构（应使用 yida-create-form-page）。
yida-custom-page
by openyida宜搭自定义页面 JSX 开发规范。React 16 宜搭原生 export function 页面模式，宜搭 JS API 调用，状态管理与编码约束。不适用于：原生表单页面开发（无需 JSX），或发布页面（编写完成后需使用 yida-publish-page 发布）。
yida-dashboard
by openyida宜搭 Dashboard / 经营看板 / 管理驾驶舱 / 数据大屏专项技能。把表单、流程、报表或业务数据沉淀成可直接交付给高管汇报的组织内看板页面，默认包含真实数据接入、日期/财年/维度筛选、多端友好布局、卡片截图、组织内短链接、隐藏导航，以及按需打通「saveFormData → 集成自动化 → 待办2.0 连接器」的真实钉钉待办闭环。当用户提到「dashboard」「Dashboard」「看板」「经营看板」「业务看板」「管理驾驶舱」「经营驾驶舱」「数据大屏」「领导驾驶舱」「高层汇报」「指标卡截图」「组织内短链」「隐藏导航」「钉钉待办」等场景时，优先使用此技能；具体 ECharts 图表实现按需调用 yida-chart。
yida-data-management
by openyida宜搭数据管理。表单实例/子表/流程实例/任务中心的查询、新增、更新。表单走 /v1/form/，流程走 /v1/process/，不能混用。
yida-db-seq-fix
by openyidaPostgreSQL Sequence 自动修复工具。检测并修复宜搭环境检测自动建表时 Sequence 起始值问题，避免主键冲突。当用户提到"Sequence"、"主键冲突"、"自增ID错误"、"db-seq-fix"时触发。
yida-density
by openyida宜搭自定义页面信息密度设计规范。提供紧凑、舒适、宽松三种密度模式的样式模板，支持密度切换和响应式降级，帮助 AI 生成符合场景需求的页面布局。不适用于：非列表/表格类页面（单卡片、表单提交页无需密度设计），或原生报表页面（密度由报表组件自身控制）。
yida-export-conversation
by openyida导出 AI 对话记录，生成结构化的 Markdown 文档。支持 Claude Code 自动检测，其他环境通过 --input 手动指定。不适用于：导出宜搭表单数据（应使用 yida-data-management），或导出应用配置（应使用 openyida export-app 命令）。
yida-flash-note-to-prd
by openyida闪记转 PRD。从钉钉闪记（AI 听记）、会议记录、需求文档中提取产品需求，生成结构化的 PRD 文档。支持文本、图片、链接三种输入方式，三层 Prompt 架构确保高质量提取。不适用于：直接创建宜搭应用（PRD 生成后还需 yida-app 流程），或处理非需求类文档（如技术文档、代码文件）。
yida-form-permission
by openyida宜搭表单权限组管理。查询、新增权限组，配置成员/数据权限/操作权限/字段权限。不适用于：配置页面公开访问分享（应使用 yida-page-config），或配置流程审批节点的字段权限（应使用 yida-process-rule）。
yida-formula-evaluate
by openyida静态检查宜搭公式语法、字段引用和常见风险。适用于用户要求“检查公式”“公式是否能用”“定位公式报错”。不适用于：声明平台真实运行结果、保存表单配置或编写完整公式字段方案。
yida-get-schema
by openyida获取表单的完整 Schema 结构，用于确认字段 ID（fieldId）和组件配置。不适用于：查询表单数据记录（应使用 yida-data-management），或修改表单字段结构（应使用 yida-create-form-page）。
yida-i18n
by openyida应用多语言管理。查询和维护宜搭应用的语言配置、文案词条、翻译状态、一键翻译和多语言升级。适用于全球化设置 / 多语言管理页面；需要应用已开通国际化能力包，或运行在 Global YiDA 环境（默认入口为 www.yidaapps.com，且支持客户自定义二级域名）。
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.