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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当需要操作 PDF 文件时使用：读取/提取 PDF 内容、PDF 转文字、合并/拆分 PDF、 填写 PDF 表单、加水印、OCR 扫描件、PDF 加密解密。 当用户说「读取PDF」「提取PDF内容」「合并PDF」「PDF转文字」「处理PDF」「OCR」时，立即使用此技能。
ai-pm-analyze
by K3tty5555需求分析技能。深入分析产品需求，挖掘用户画像、核心痛点、功能范围和优先级。 通过对话引导用户补充信息，不满足于表面描述。 当用户说「分析需求」「帮我想清楚这个需求」「需求到底解决什么问题」「用户痛点是什么」 「需求挖掘」「功能分析」「做用户画像」「这个需求值不值得做」时，立即使用此技能。
ai-pm-driver
by K3tty5555PM 风格 lint 命令入口（评审前最后一道关）。 本 skill 是 `pm-agent` 的 thin wrapper——接收 PRD 文件路径，调用 pm-agent 用 lint mode 扫描整份 PRD，输出 punch list（越界 + 缺失 + 篇幅）。 与 `pm-agent` 的关系：driver = 命令糖衣 / pm-agent = 真正的判断引擎。判断卡 / 反例 / 越界规则单一事实源在 pm-agent，driver 不重复维护。 与 `ai-pm-review`（六角色评审会）和 `multi-perspective-review`（多视角技术审查）的区别：driver 是 PM 个人风格 lint，5 分钟出结论。 当用户说「PM 守门」「审视 PRD」「PRD 挑刺」「PRD 越界检查」「driver 一下」「PRD 自审」「评审前体检」「历史 PRD 回归」时，使用此 skill。
ai-pm-interview
by K3tty5555现场调研技能。适用于线下与客户面对面沟通场景，支持结构化访谈、实时记录和快速生成 PRD。 当用户说「现场调研」「客户访谈」「用户访谈」「拜访客户」「面对面沟通」「帮我设计访谈问题」 「用户研究」「访谈提纲」「带客户调研」时，立即使用此技能。
ai-pm-knowledge
by K3tty5555产品知识库管理技能。沉淀方法论、决策记录、踩坑经验，下次遇到类似问题时自动推荐。 当用户说「保存经验」「记录决策」「沉淀知识」「踩坑记录」「搜索知识库」 「之前有没有遇过类似问题」「知识管理」「经验总结」「记下来」时，立即使用此技能。
ai-pm-persona
by K3tty5555产品分身技能。分析你的历史需求文档，学习你的写作风格、措辞习惯和结构偏好。让 AI 生成的 PRD 越来越像你写的。 当用户说「学习我的风格」「让PRD像我写的」「分析我的文档」「风格设置」「个性化PRD」 「我想让输出更像我的语气」「训练分身」「风格模仿」时，立即使用此技能。
ai-pm-prd
by K3tty5555PRD 生成技能。整合需求分析、竞品研究、用户故事，输出完整的产品需求文档。支持产品分身写作风格和设计规范。 当用户说「生成PRD」「写PRD」「产品需求文档」「需求文档」「功能规格书」「输出PRD」 「帮我写需求」「把需求整理成文档」时，立即使用此技能。
ai-pm-prototype
by K3tty5555原型生成技能。基于 PRD 生成可交互的单页网页原型，支持移动端和 Web 端。 首次生成时询问设计规范（公司规范 / AI 情境定制 / 主流组件库），项目内记住偏好。 若项目存在 Codex 生成的视觉锚点包（06-prototype-visual/manifest.json），生成 HTML 前必须读取并遵循。 当用户说「生成原型」「做原型」「可交互原型」「HTML原型」「页面原型」「低保真」「高保真原型」 「画个界面」「把PRD做成原型」时，立即使用此技能。 边界：本技能用于「把已有 PRD/需求做成可评审原型」；脱离 PRD 的纯视觉探索、通用 UI 组件生成或视觉精修，可使用外部 impeccable 增强，但 AI_PM 原型默认以 ai-pm-frontend-design 为本地设计内核。
ai-pm-review
by K3tty5555PRD 或原型已写完，需要模拟正式评审会议、让六大角色出评审意见时使用（区别于 multi-perspective-review 的设计阶段审查）。 当用户说「评审PRD」「需求评审」「PRD有没有问题」「帮我挑毛病」「技术评审」「开评审会」 「PRD走查」「需求质量检查」「PRD评审报告」时，立即使用此技能。
ai-pm
by K3tty5555当需要从零开始走完完整产品立项流程（需求→分析→竞品→用户故事→PRD→原型→评审）时使用。 支持多项目管理和断点续传，复杂需求可启用多代理协作。 当用户说「我有个产品想法」「帮我做个产品」「从零开始做需求」「全流程出PRD」 「做一个App/小程序/系统」「产品立项」「继续上次的项目」「切换项目」时，立即使用此技能。
multi-perspective-review
by K3tty5555设计方案或实施计划完成后、定稿前需要多角色质量把关时使用（不是 PRD 正式评审会）。 当用户说「审视一下」「帮我审查」「检查这个设计」「review 一下」「看看有没有问题」 「多视角审视」「专家审查」「设计有没有漏洞」时触发。 边界：审查对象是「设计方案/实施计划」。若用户要审的是 PRD——正式评审会用 ai-pm-review，PM 风格 lint 用 ai-pm-driver，不要用本技能。
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.