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...
pseo-metadata
by lisbeth718Implement fully dynamic SEO metadata including title tags, meta descriptions, canonical URLs, Open Graph tags, and Twitter cards for programmatic SEO pages. Use when setting up or fixing metadata, adding OG/Twitter tags, implementing canonical URLs, or when metadata is static, duplicated, or missing across pSEO pages.
pseo-audit
by lisbeth718Audit and assess a codebase for programmatic SEO readiness at 1000+ page scale. Use when starting a pSEO project, evaluating an existing codebase for pSEO gaps, or when the user asks to audit, assess, or review their site for programmatic SEO scalability.
pseo-data
by lisbeth718Design and implement the structured data architecture that powers programmatic SEO pages, including content models, data sources, slug generation, and data-fetching layers. Use when setting up or refactoring the data foundation for pSEO, designing content models, or building the data pipeline that feeds page templates.
pseo-discovery
by lisbeth718Analyze a codebase and business context to discover programmatic SEO opportunities, identifying what page types to generate, what data assets exist, and what search intent can be matched at scale. Use when starting a new pSEO project, when the user isn't sure what to build programmatically, or when exploring what structured data exists in the codebase or business that could power scalable pages.
pseo-linking
by lisbeth718Build an intelligent internal linking system using hub-and-spoke structures, related page suggestions, breadcrumb navigation, and topical clustering for programmatic SEO. Use when implementing or improving internal linking, adding breadcrumbs, building category hub pages, or creating related-pages components.
pseo-llm-visibility
by lisbeth718Optimize programmatic SEO pages for visibility and citation in AI-generated answers from ChatGPT, Perplexity, Google AI Overviews, and other LLM-powered search. Use when optimizing for LLM citation, implementing llms.txt, configuring AI crawler access, structuring content for AI extraction, or when the user asks about generative engine optimization (GEO), AI search visibility, or getting cited by AI.
pseo-orchestrate
by lisbeth718Orchestrate the full programmatic SEO implementation by coordinating all pseo-* skills in the correct order. Use when implementing pSEO from scratch, running the full pSEO pipeline, or when the user asks to "set up programmatic SEO" or "build pSEO pages" without specifying a single skill.
pseo-performance
by lisbeth718Optimize a programmatic SEO application for Core Web Vitals, build performance, and scalability at 1000+ pages, including static generation, incremental regeneration, bundle optimization, and caching. Use when builds are slow, pages are large, Core Web Vitals scores are poor, or when scaling to many pages causes performance issues.
pseo-quality-guard
by lisbeth718Validate programmatic SEO pages against quality standards to prevent thin content, duplicate content, and keyword cannibalization. Use when auditing pSEO output quality, before deploying new pages, when Google Search Console reports issues, or when checking if generated pages meet quality thresholds. This skill can also be used automatically to validate changes made by other pseo-* skills.
pseo-scale
by lisbeth718Architect programmatic SEO systems for 10K-100K+ pages with database-backed data layers, data sufficiency gating, incremental validation, crawl budget management, content enrichment pipelines, and edge delivery. Use when scaling beyond 10K pages, when builds are OOMing, when Google is not indexing all pages, or when the current in-memory architecture has hit its limits.
pseo-schema
by lisbeth718Implement JSON-LD structured data and schema markup for programmatic SEO pages, including Article, FAQ, Breadcrumb, Product, and other context-specific schema types. Use when adding or fixing schema markup, implementing structured data, or when Google Search Console reports schema errors.
pseo-templates
by lisbeth718Create page templates with dynamic routing for programmatic SEO, including unique intent-matched content per page with differentiated titles, headings, descriptions, and FAQs. Use when building or refactoring pSEO page templates, setting up dynamic routes, or ensuring each generated page has unique, valuable content.
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.