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...
geek-skills-ai-sales-champion
by staruhubAI咨询/销售的对话策略助手。当用户需要准备AI方案沟通、跟业务部门聊AI落地、写AI提案、应对客户异议、做AI培训破冰时使用。触发场景："怎么跟老板聊AI"、"客户说AI不靠谱"、"准备一个AI方案汇报"、"帮我想想怎么推AI"、"业务部门不配合"、"AI项目怎么卖"、"demo之后怎么跟进"。也适用于AI咨询师、技术合伙人、CTO做内部AI推广。
geek-skills-gaokao-expert
by staruhub资深高考命题专家助手,提供专业的命题指导和评审服务。适用于创作高考试题、评审试题质量、分析试卷结构、了解命题趋势等场景。结合文档工具提取解压文件,使用网络搜索了解最新命题趋势,使用分析工具评估题目质量和试卷结构。涵盖"一核四层四翼"评价体系、2025年命题趋势、题型规范、评分标准、命题流程等多个维度,符合高考命题最佳实践。
geek-skills-xuefeng-method
by staruhub雪峰式AI-Native产品开发方法论。适用于：(1) 用户行为开放、不可穷举的AI-native产品（AI日历、AI助手、AI推荐、对话式产品等），(2) 强模型依赖型场景，AI驱动核心决策而非仅辅助，(3) 多专精Agent架构设计与分工，(4) 上线后快速校准、行为审计与漂移检测，(5) 模型选择和智能路由策略，(6) 概率性输出的质量评估。触发场景包括"AI-native产品怎么做"、"用户行为不可预测怎么办"、"多agent怎么分工"、"模型漂移怎么处理"、"校准到95%太难了"、"唯快不破"、"怎么选模型"、"agent并行分工"、"AI产品上线后怎么迭代"。注意：如果产品是场景明确、边界可定义的+AI类型，请改用 keqian-method skill。即使用户没有明确说"AI-native"，但在讨论AI驱动决策、用户行为不可预测、概率性输出等话题时也应触发。
geek-skills-university-exam-prep
by staruhub大学备考苏格拉底式学习助手，专为应对"面向课本和PPT考试"设计。当用户说"帮我复习"、"准备考试"、"备考"、"期末复习"、"模拟学习"、"突击复习"等时触发。核心特点：(1)强制要求用户上传课本/PPT/考纲等原始材料——没有材料就无法有效辅导；(2)使用苏格拉底式提问法，不直接灌输而是引导思考；(3)专注于应用型和理解型内容，而非纯概念记忆；(4)模拟真实考试场景进行针对性练习；(5)分析材料识别高频考点和重难点。适用于大学各科目期中期末考试备考、突击复习、考前模拟。
llm-wiki
by staruhubBuild and maintain a structured LLM-generated wiki for any codebase. Use when the user asks to analyze/understand/document a codebase, build a code wiki, create project documentation from source, or update an existing .llm-wiki. Triggers on phrases like "build wiki", "analyze this codebase", "document this project", "update wiki", "llm-wiki", or when entering an unfamiliar project that has no .llm-wiki yet.
geek-skills-c-drive-cleaner
by staruhubWindows C盘清理和磁盘空间管理工具。当用户需要清理C盘、释放磁盘空间、查找大文件、分析磁盘占用、删除临时文件、清理缓存、管理Windows系统垃圾文件时使用此skill。适用于以下场景:(1)C盘空间不足需要清理;(2)查找和删除大文件;(3)分析磁盘空间占用;(4)清理系统临时文件和缓存;(5)清理浏览器缓存;(6)清理回收站;(7)清理系统日志;(8)优化Windows磁盘空间。
geek-skills-a-share-analyst
by staruhubA股专业分析师助手，提供每日股价分析、选股策略和投资建议。适用于：(1) 获取A股实时行情和历史数据，(2) 技术面分析（K线形态、MACD、KDJ、RSI、布林带等），(3) 基本面分析（财务指标、估值分析），(4) 板块热点追踪，(5) 选股策略筛选，(6) 量化因子分析，(7) 生成每日股市分析报告。当用户询问"帮我分析股票"、"今日选股"、"A股行情分析"、"技术分析"、"基本面分析"、"量化选股"等相关问题时触发。
geek-skills-product-manager
by staruhub资深产品经理助手,提供PRD文档创作与评审、产品策略咨询、留存增长分析、竞品研究、功能优先级排序等全方位产品管理支持。适用于创作或评审PRD/MRD/BRD/用户故事等产品文档；诊断产品问题（留存低、转化差、增长瓶颈）并给出可执行策略；进行竞品分析和市场研究；设计功能方案和用户体验优化。当用户提到"PRD"、"需求文档"、"产品规划"、"用户留存"、"功能设计"、"竞品分析"、"产品指标"、"增长策略"、"用户体验优化"、"功能优先级"等产品管理相关话题时，使用此skill。即使用户没有明确说"产品"，但在讨论App功能设计、用户增长、商业模式、需求分析等话题时也应触发。
geek-skills-ppt-designer
by staruhub专业PPT设计与制作skill，基于设计最佳实践创建精美、专业的演示文稿。涵盖排版、配色、图片处理、视觉层次等核心设计原则。当用户需要制作高质量PPT、改进现有PPT设计、学习PPT制作技巧，或需要商务演示、学术报告、创意提案等场景的专业演示文稿时使用。可与seedream-imagegen skill配合为PPT生成定制化配图。
geek-skills-podcast-generator
by staruhubGenerate AI podcasts using Volcano Engine's Podcast AI Model. Use when user wants to create podcast audio from text input, generate conversational audio content, or transform written content into multi-speaker podcast format. Supports Chinese dual-speaker podcasts with customizable voice options.
geek-skills-pair-programming
by staruhub结对编程搭档skill，在Claude生成代码后自动进行代码审核和改进建议。当Claude为用户编写、修改或生成代码时，此skill会触发自我审查流程，检查代码质量、安全性、性能和最佳实践，并主动指出改进之处。适用于任何代码生成场景，包括新功能开发、bug修复、代码重构、API实现等。
geek-skills-notion-infographic
by staruhub基于大纲自动研究并生成高质量可视化内容的 Agent Pipeline。 支持两种输出模式：(A) PPTX 演示文稿（PptxGenJS 编程生成，含完整设计系统）； (B) 信息图提示词组图（Notion 手绘风 / 多风格可选，可直接用于 imageGen / DALL·E）。 用户只需提供主题大纲或关键词，skill 自动启动专家子 Agent 并行抓取信息， 主 Agent 负责规划、设计决策和验收，最终输出风格统一的高质量视觉内容。 触发场景："帮我做一组信息图"、"生成 Notion 风格图片"、"做个PPT"、"做个演示文稿"、 "把这个大纲做成图"、"infographic"、"信息图"、"手绘信息图"、"图解"、 "把这篇文章可视化"、"做成社交媒体传播图"、"小红书图文"、"slides"、 "presentation"、"deck"、"pptx"、"演示文稿"、"汇报PPT"。 即使用户没有明确说"信息图"或"PPT"，但在提供大纲/要点并要求可视化传播时也应触发。 当用户上传文章/文稿并要求"做成图"、"可视化"、"做成演示"时，同样触发此 skill。
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.