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...
seo-content-structure
by jrr996shujin-png内容结构诊断子模块。检查页面的 Meta 标签（title/description）质量、H1-H4 标题层级、列表独立性、结构化表格、FAQ 格式。当用户提到"标题标签""meta description""内容结构""heading 层级""FAQ 优化"时触发。这些检查直接影响 AI 引擎对页面内容的解析和引用能力。
aeo-content-strategy
by jrr996shujin-pngAEO/GEO content strategy skill that combines Reddit and Quora monitoring, long-tail question mining, and content topic recommendations into one actionable report. Use this skill whenever the user wants to: discover what people are asking about their product category on Reddit or Quora, find unanswered or low-competition long-tail questions for AEO optimization, generate blog topic ideas based on real community discussions and search intent, build a content calendar targeting AI citation opportunities, understand what questions AI tools like ChatGPT/Perplexity/Gemini might pull answers for, do competitive content gap analysis for AI search visibility, or plan content that targets specific user decision-making questions. Also trigger when user mentions 'AEO', 'answer engine optimization', 'AI search visibility', 'what should I write about', 'content gaps', 'Reddit monitoring', 'community listening', 'long-tail keywords for AI', or 'content strategy for LLM citations'. This skill produces a comprehensive report
seo-content-quality-signals
by jrr996shujin-png内容质量信号诊断子模块。检查页面的作者信息（E-E-A-T 信号）、发布/更新日期、页面内容字数和可读性。当用户提到"内容质量""E-E-A-T""作者权威性""内容新鲜度""可读性分析"时触发。这些信号在 AI 引擎选择引用来源时权重越来越高。
seo-multimedia-accessibility
by jrr996shujin-png多媒体可访问性诊断子模块。检查图片 alt 文本覆盖率和质量、视频转录文字/字幕可用性。当用户提到"图片 SEO""alt 标签""视频 SEO""无障碍访问""图片替代文字"时触发。这直接影响 AI 引擎对多媒体内容的理解能力。
seo-search-accessibility
by jrr996shujin-png搜索引擎可访问性诊断子模块。检查网站对搜索引擎爬虫和 AI 引擎（GPTBot、Bingbot、Googlebot）的可访问性，包括 Robots.txt 配置、XML Sitemap、HTML vs JS 渲染方式、Canonical 标签设置。当用户提到"爬虫抓不到""robots.txt""sitemap""网站没被收录""AI 搜索找不到我的网站"时触发。
seo-site-architecture
by jrr996shujin-png站内架构诊断子模块。检查网站的内部链接健康度和核心页面点击深度。当用户提到"内部链接""网站架构""页面深度""链接结构""孤岛页面""导航结构"时触发。良好的站内架构帮助搜索引擎和 AI 引擎高效地发现和理解网站全部内容。
seo-aeo-diagnostics
by jrr996shujin-png网站 SEO/AEO 基础诊断技能。当用户要求对网站做 SEO 基础诊断、技术审计、AEO 可访问性检查、或想了解网站在搜索引擎和 AI 引擎面前的健康状况时，使用此技能。即使用户只是说'帮我看看这个网站有什么问题'或'检查一下我的网站'，也应触发。此技能编排 7 个子模块执行完整诊断：技术基础、搜索引擎可访问性、结构化数据、内容结构、多媒体可访问性、内容质量信号、站内架构。
seo-structured-data
by jrr996shujin-png结构化数据诊断子模块。检查网站的 Schema Markup（Organization、Article、FAQ 等）、Open Graph 标签、Twitter Card 标签、hreflang 多语言标签。当用户提到"结构化数据""schema""rich snippet""社交分享卡片""og 标签""多语言 SEO"时触发。
seo-technical-foundation
by jrr996shujin-pngSEO/AEO 技术基础诊断子模块。检查网站的技术健康状况，包括死链扫描、页面加载速度分析、移动端适配检测、HTTPS 安全配置。当用户提到"网站打不开""页面很慢""手机上显示不对""SSL 证书"或任何技术层面的网站问题时触发。也可作为完整诊断流程的第一个子模块被编排器调用。
seo-competitor-content
by jrr996shujin-png竞品内容监测子模块。每周扫描竞品网站的新发布内容（Blog、Docs、Learning、Updates、Help Center），追踪竞品文章排名趋势和估算流量，对排名上升的文章做深度分析。当用户提到'竞品发了什么新东西''竞品的博客''对手最近在做什么内容''竞品排名上升了'时触发。数据源：SEMrush API + web_fetch。
seo-content-performance
by jrr996shujin-png品牌内容表现追踪子模块。每周追踪品牌网站每篇文章的排名、流量、用户行为数据，自动标记流量持续下降的衰退内容，生成更新提醒。当用户提到'文章表现怎么样''哪篇文章流量掉了''需要更新的内容''内容衰退''上周发的文章怎么样了'时触发。数据源：Google Search Console API + Google Analytics API + SEMrush API。
seo-keyword-ranking
by jrr996shujin-png关键词监测与排名追踪子模块。追踪品牌和竞品在目标关键词上的 Google 和 Bing 排名变化，按类别分组管理关键词，排名大幅下滑时自动报警。当用户提到'排名掉了''关键词排名''竞品排在哪''加个新关键词''哪些词我们还没覆盖'时触发。数据源：SEMrush API + Google Search Console API。
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.