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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screenplay-elements
by MadBomberUse when writing, reviewing, or analyzing a screenplay — evaluating character depth, story motivation, plot construction, narrative structure, or dramatic conflict. Also use when a screenplay feels flat, aimless, or lacks tension.
rails-audit-thoughtbot
by MadBomberPerform comprehensive code audits of Ruby on Rails applications based on thoughtbot best practices. Use this skill when the user requests a code audit, code review, quality assessment, or analysis of a Rails application. The skill analyzes the entire codebase focusing on testing practices (RSpec), security vulnerabilities, code design (skinny controllers, domain models, PORO with ActiveModel), Rails conventions, database optimization, and Ruby best practices. Outputs a detailed markdown audit report grouped by category (Testing, Security, Models, Controllers, Code Design, Views) with severity levels (Critical, High, Medium, Low) within each category.
vanilla-rails
by MadBomberDesign and review Rails applications using Vanilla Rails philosophy from 37signals/Basecamp. Emphasizes thin controllers, rich domain models, and avoiding unnecessary service layers. Use when analyzing Rails codebases, reviewing PRs, or refactoring toward simpler architecture. Triggers on "service layer", "service object", "thin controller", "rich model", "vanilla rails", "dhh style", "over-engineering", "unnecessary abstraction".
dhh-rails-style
by MadBomberWrite Ruby and Rails code in DHH's distinctive 37signals style. Use this skill when writing Ruby code, Rails applications, creating models, controllers, or any Ruby file. Triggers on Ruby/Rails code generation, refactoring requests, code review, or when the user mentions DHH, 37signals, Basecamp, HEY, or Campfire style. Embodies REST purity, fat models, thin controllers, Current attributes, Hotwire patterns, and the "clarity over cleverness" philosophy.
layered-rails
by MadBomberDesign and review Rails applications using layered architecture principles from "Layered Design for Ruby on Rails Applications". Use when analyzing Rails codebases, reviewing PRs for architecture violations, planning feature implementations, or implementing patterns like authorization, view components, or AI integration. Triggers on "layered design", "architecture layers", "abstraction", "specification test", "layer violation", "extract service", "fat controller", "god object".
rubyllm-tribunal
by MadBomberLLM evaluation and testing for RubyLLM. Use this skill when you need to verify AI response quality, test for hallucinations, check safety, run red team attacks, or integrate LLM-as-judge assertions into your test suite.
rubyllm-tools
by MadBomberFunction calling for RubyLLM. Use this skill when creating tools that let AI call your Ruby code, declaring parameters with the params DSL, using tools in chat, monitoring tool calls with callbacks, handling tool security, and implementing advanced patterns like halt, provider-specific parameters, and concurrent tool execution (v1.16+).
rubyllm
by MadBomberOne beautiful Ruby API for GPT, Claude, Gemini, and more. Use this skill when building AI-powered applications with RubyLLM - chatbots, AI agents, RAG applications, content generators, vision/audio analysis, embeddings, image generation, and Rails integration. Supports 15+ providers with a unified interface. v1.16 adds concurrent tool execution (threads or fibers), built-in instrumentation, and per-provider API base URL overrides.
rubyllm-schema
by MadBomberRuby DSL for JSON Schema creation. Use this skill when defining structured data schemas for LLM function calling or structured outputs with RubyLLM. Provides Rails-inspired API for creating complex nested schemas.
rubyllm-red-candle
by MadBomberLocal LLM execution with quantized GGUF models for RubyLLM. Use this skill when running models locally for zero latency, no API costs, complete privacy, and offline capability. Supports Metal (macOS), CUDA (NVIDIA), and CPU.
rubyllm-rails
by MadBomberRails integration for RubyLLM. Use this skill when setting up ActiveRecord-backed chats, Hotwire/Turbo streaming, background job processing, chat UI generation, agents in Rails, file attachments with ActiveStorage, and multi-tenant LLM contexts.
rubyllm-opentelemetry
by MadBomberOpenTelemetry tracing for RubyLLM. Use this skill when you need observability into LLM applications with support for Langfuse, Datadog, Honeycomb, Jaeger, Arize Phoenix, and any OpenTelemetry-compatible backend.
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.