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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xiaohongshu-note-creator
by wpsnote【笔记/文章转小红书】将用户已有的 WPS 笔记或文章内容，改写压缩为小红书图文方案。 核心特征：用户已有原文内容（笔记、文章、推文、长文），需要转换格式发小红书。 触发词："把笔记做成小红书""把这篇文章转小红书""把这篇推文发小红书""帮我生成小红书图文""把这篇笔记发小红书""笔记转小红书""小红书图文""生成小红书""这篇文章改成小红书""这个内容发小红书"。 不适用于：从0到1写内容（用 content-creator）、纯文案排版（用 wechat-publisher）、没有已有内容只想"写小红书"（用 content-creator）。
misconception-finder
by wpsnote当用户希望检查一篇 WPS 学习笔记里是否存在理解错误、概念混淆、逻辑跳步或表述过虚时使用此 Skill。适合课后自查、复习前校正、讲给别人之前自检，以及“我是不是以为自己懂了其实没懂”的场景。
class-note-builder
by wpsnote当用户希望把课堂逐字稿、OCR 笔记、截图资料或零散学习内容整理成结构化的 WPS 学习笔记时使用此 Skill。适合课堂记录整理、培训复盘、补课笔记汇总、课程主笔记构建等场景，尤其适合原始材料很乱、但最终需要一篇可复习、可回看、可继续补充的主笔记时。
content-digest
by wpsnote将任意内容提炼为结构化知识笔记，自动保存到 WPS 笔记。只要用户给出任何内容（链接、图片、本地文件、粘贴文字）并有保存笔记的意图，就应使用此 skill。常见触发词：「总结」「提炼」「做笔记」「读书笔记」「学习笔记」「整理成笔记」「帮我看看」「帮我解读」「记一下」「存下来」「整理一下」「帮我归纳」。也适用于用户直接给出 URL、@图片、@文件但没有明确说「存笔记」的情况——只要内容值得保存，主动使用此 skill。支持网页、公众号、本地 PDF/Word/TXT/Markdown/图片/截图、粘贴文本；所有图片（本地截图、网络图片、PDF 图片页、笔记内图片）均可自动视觉解读；输出一句话概括、核心观点、金句摘录、我的思考；多篇合并为一篇笔记。不用于：代码重构、纯技术问答、文件编辑、仅讨论链接（无保存意图）、日常聊天。
ie-engine
by wpsnote灵感引擎的统一入口，串联记忆检索、想法连接和洞见生成的完整流水线。当用户提到"灵感引擎"、"激发灵感"、"connect the dots"、"寻找灵感"、"帮我把想法串起来"、"从笔记中发现新东西"等需要完整运行灵感生成流水线的场景时使用此技能。此外，当用户正在深入探索某个主题、进行创造性思考、或讨论复杂问题时，如果你判断用户的历史笔记中可能存在相关启发，也应主动考虑触发此技能，以轻量提示的方式提醒用户历史笔记中可能有相关灵感。只有需要完整流水线时才触发此技能；如果用户只需要单一能力（如仅检索笔记或仅分析关系），请使用对应的分层技能：ie-retrieve-memory、ie-connect-dots 或 ie-generate-insight。
lecture-focus-extractor
by wpsnote当用户手上已经有一篇较长的课堂笔记、逐字稿或学习记录，但只想提取最值得复习的重点时使用此 Skill。适合课程录音整理、复习提纲抽取、考前过重点、快速补课等场景。
paper-researcher
by wpsnote学术论文全流程助手：搜索论文、下载 PDF、存入 WPS 笔记、精读分析。当用户说"搜论文"、"找论文"、"下载论文"、"读论文"、"帮我找 paper"、"搜一下 XXX 相关的论文"、"把这篇论文存到笔记"、"分析这篇论文"、"帮我做文献调研"时触发。支持 arXiv 和 OpenAlex 两个数据源，自动完成搜索→下载→转文本→写入 WPS 笔记→分析的完整闭环。
prerequisite-gap-finder
by wpsnote当用户觉得一个主题看不懂、学得卡住，或者想知道自己到底缺了哪些前置基础时使用此 Skill。适合课程复习、自学卡点分析、考前查漏补缺、回看旧笔记时发现“明明记了但还是不会”的场景。
ie-generate-insight
by wpsnote将推理分析结果转化为可阅读的洞见文本，生成下一步探索建议，展示想法的演化路径。当用户提到"生成洞见"、"给我灵感"、"启发我"、"下一步探索什么"、"想法是怎么演变的"等需要从分析结果中提炼启发性内容的场景时使用此技能。此技能属于灵感引擎（ie-）洞见层，专门负责将结构化推理结果转化为人类可读的灵感内容，不涉及笔记检索或关系分析。
ie-retrieve-memory
by wpsnote从用户的 WPS 笔记中检索历史知识和过去的想法。当用户提到"回忆过去的笔记"、"之前写过什么"、"历史想法"、"以前的笔记"、"查找旧笔记"等需要从笔记库中找回过往知识的场景时使用此技能。此技能属于灵感引擎（ie-）记忆层，专门处理笔记知识检索，不涉及通用文件搜索或代码查找。
ie-connect-dots
by wpsnote对笔记和想法进行语义聚类、发现想法之间的隐含连接、识别长期重复出现的主题模式。当用户提到"连接想法"、"发现关联"、"想法聚类"、"找到模式"、"这些笔记有什么联系"等需要理解多条笔记之间关系的场景时使用此技能。此技能属于灵感引擎（ie-）推理层，专门处理想法之间的关系分析，不涉及笔记检索或内容生成。
wpsnote-beautifier
by wpsnote智能美化 WPS 笔记文档，采用克制统一的配色风格（全文仅1种主色调，不混用多色系）。核心能力：优化标题层级结构、用高亮块强调核心结论与注意事项、用分栏展示对比或并列内容、应用统一配色方案并写入。仅当用户明确表达美化需求时才触发，例如：美化笔记、排版优化、文档美化、笔记排版、WPS笔记美化、智能排版、文档结构调整、加颜色、加高亮、加分栏、让笔记好看点、优化文档格式、笔记太丑了、调整排版、加点样式、给笔记润色、整理笔记格式、提升可读性。不要在用户仅要求写入内容、编辑文字、总结归纳等非美化场景下主动触发此skill。通过 user-wpsnote MCP 服务操作 WPS 笔记文档。
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.