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...
wheels-migration-generator
by wheels-devGenerate database-agnostic Wheels migrations for creating tables, altering schemas, and managing database changes. Use when creating or modifying database schema, adding tables, columns, indexes, or foreign keys. Prevents database-specific SQL and ensures cross-database compatibility.
wheels-api-generator
by wheels-devGenerate RESTful API controllers with JSON responses, proper HTTP status codes, and API authentication. Use when creating API endpoints, JSON APIs, or web services. Ensures proper REST conventions and error handling.
wheels-controller-generator
by wheels-devGenerate Wheels MVC controllers with CRUD actions, filters, parameter verification, and proper rendering. Use when creating or modifying controllers, adding actions, implementing filters for authentication/authorization, handling form submissions, or rendering views/JSON. Ensures proper Wheels conventions and prevents common controller errors.
wheels-debugging
by wheels-devTroubleshoot common Wheels errors and provide debugging guidance. Use when encountering errors, exceptions, or unexpected behavior. Provides error analysis, common solutions, and debugging strategies for Wheels applications.
wheels-documentation-generator
by wheels-devGenerate documentation comments, README files, and API documentation for Wheels applications. Use when documenting code, creating project READMEs, or generating API docs.
wheels-model-generator
by wheels-devGenerate Wheels ORM models with proper validations, associations, and methods. Use when the user wants to create or modify a Wheels model, add validations, define associations (hasMany, belongsTo, hasManyThrough), or implement custom model methods. Prevents common Wheels-specific errors like mixed argument styles and ensures proper CFML syntax.
wheels-auth-generator
by wheels-devGenerate authentication system with user model, sessions controller, and password hashing. Use when implementing user authentication, login/logout, or session management. Provides secure authentication patterns and bcrypt support.
wheels-refactoring
by wheels-devRefactor Wheels code for better performance, security, and maintainability. Use when optimizing code, fixing anti-patterns, improving performance, or enhancing security. Provides refactoring patterns and best practices.
wheels-anti-pattern-detector
by wheels-devAutomatically detect and prevent common Wheels framework errors before code is generated. This skill activates during ANY Wheels code generation (models, controllers, views, migrations) to validate patterns and prevent known issues. Scans for mixed arguments, query/array confusion, non-existent helpers, and database-specific SQL.
wheels-deployment
by wheels-devConfigure Wheels applications for production deployment with security hardening, performance optimization, and environment-specific settings. Use when preparing for production, configuring servers, or hardening security.
wheels-test-generator
by wheels-devGenerate TestBox BDD test specs for Wheels models, controllers, and integration tests. Use when creating tests for models (validations, associations), controllers (actions, filters), or integration workflows. Ensures comprehensive test coverage with proper setup/teardown and Wheels testing conventions.
wheels-view-generator
by wheels-devGenerate Wheels view templates with proper query handling, form helpers, and association display. Use when creating or modifying views, forms, layouts, or partials. Prevents common view errors like query/array confusion and incorrect form helper usage. Handles index views, show views, form views, and layouts with proper CFML syntax.
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.