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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entity-optimizer
by aaron-he-zhuUse when the user asks to "optimize entity presence"; builds Knowledge Graph, Wikidata, sameAs, and AI recognition signals for a canonical entity identity. Not for page-level AI-citation readiness — use geo-content-optimizer. 实体优化/知识图谱
on-page-seo-auditor
by aaron-he-zhuUse when the user asks to "audit on-page SEO" or "diagnose why a single page dropped"; scores titles, meta, header structure, keyword placement, links, and images with prioritized fixes. For E-E-A-T / publish-readiness scoring use content-quality-auditor; for crawl / CWV / indexing use technical-seo-checker. 页面SEO审计/排名诊断
keyword-research
by aaron-he-zhuUse when the user asks to "find keywords", "挖词", or "搜什么词"; prioritizes search volume, keyword difficulty, intent, and topic clusters from provided or connected data. Not for competitor-relative coverage gaps — use content-gap-analysis. 关键词研究/内容选题
alert-manager
by aaron-he-zhuUse when the user asks to "set SEO alerts" or "排名掉了提醒我"; configures threshold notifications for FUTURE ranking, traffic, technical, and competitor changes. Not for one-time measurement or reporting — use rank-tracker or performance-reporter. SEO预警/排名监控
backlink-analyzer
by aaron-he-zhuUse when the user asks to "analyze backlinks" or "外链分析"; profiles external referring domains, anchor-text distribution, toxic links, and competitor link gaps. Not for internal links — use internal-linking-optimizer. 外链分析/反向链接
internal-linking-optimizer
by aaron-he-zhuUse when the user asks to "fix internal linking" or "find orphan pages"; maps link architecture, authority flow, anchor text, and crawl depth, then delivers a prioritized source/target/anchor plan. Not for external backlinks — use backlink-analyzer. 内链优化/站内架构
schema-markup-generator
by aaron-he-zhuUse when the user asks to "generate schema"; creates JSON-LD for FAQ, HowTo, Article, Product, and LocalBusiness rich-result candidates. Not for title/meta-description tags — use meta-tags-optimizer; not for crawl/index technical issues — use technical-seo-checker. Schema标记/结构化数据
serp-analysis
by aaron-he-zhuUse when the user asks to "analyze the SERP" or "SERP分析"; maps SERP features, layout, ranking factors, search intent, AI Overviews, and snippet opportunities for a query. Not for keyword demand discovery — use keyword-research. SERP分析/搜索结果
geo-content-optimizer
by aaron-he-zhuUse when the user asks to "optimize for AI citations"; improves citation readiness for ChatGPT, Perplexity, AI Overviews, Gemini, and Claude. Not for structural on-page SEO — use on-page-seo-auditor; not for net-new drafting — use seo-content-writer. AI引用优化/GEO优化/AI搜索
meta-tags-optimizer
by aaron-he-zhuUse when the user asks to "optimize meta tags"; improves titles, descriptions, Open Graph, Twitter cards, and CTR test variants. Not for JSON-LD structured data — use schema-markup-generator; not for body copy — use seo-content-writer. 标题优化/元描述/CTR
seo-content-writer
by aaron-he-zhuUse when the user asks to "write SEO content"; drafts new posts, articles, and landing pages with keywords, headers, snippets, and evidence boundaries. Not for AI-citation/GEO readiness scoring — use geo-content-optimizer; not for updating decaying existing content — use content-refresher. SEO文章写作/内容优化
content-quality-auditor
by aaron-he-zhuUse when auditing content quality, E-E-A-T, or publish readiness; runs 80-item CORE-EEAT scoring with veto checks and a fix plan. Not for structural on-page tags/headers — use on-page-seo-auditor; not for domain/citation trust — use domain-authority-auditor. 内容质量/EEAT评分
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.