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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xcode-test
by ratacatBuild and test iOS apps on simulator using XcodeBuildMCP
brave-search
by ratacatUse when user asks to search the web, look something up online, find current/recent/latest information, or needs cited answers. Triggers on "search", "look up", "find out about", "what is the current/latest", image searches, news lookups. NOT for searching code/files—only for web/internet searches.
dhh-rails-reviewer
by ratacatUse this agent when you need a brutally honest Rails code review from the perspective of David Heinemeier Hansson. This agent excels at identifying anti-patterns, JavaScript framework contamination in Rails codebases, and violations of Rails conventions. Perfect for reviewing Rails code, architectural decisions, or implementation plans where you want uncompromising feedback on Rails best practices.\n\n<example>\nContext: The user wants to review a recently implemented Rails feature for adherence to Rails conventions.\nuser: "I just implemented a new user authentication system using JWT tokens and a separate API layer"\nassistant: "I'll use the DHH Rails reviewer agent to evaluate this implementation"\n<commentary>\nSince the user has implemented authentication with patterns that might be influenced by JavaScript frameworks (JWT, separate API layer), the dhh-rails-reviewer agent should analyze this critically.\n</commentary>\n</example>\n\n<example>\nContext: The user is planning a new Rails feature and wan...
lfg
by ratacatFull autonomous engineering workflow
brainstorming
by ratacatThis skill should be used before implementing features, building components, or making changes. It guides exploring user intent, approaches, and design decisions before planning. Triggers on "let's brainstorm", "help me think through", "what should we build", "explore approaches", ambiguous feature requests, or when the user's request has multiple valid interpretations that need clarification.
calci-prediction-market
by ratacatContext and working knowledge for Calci’s prediction-market domain, which is powered by Kalshi. Use this skill whenever the user asks about Calci prediction markets, Kalshi markets, tickers, order books, pricing, settlement, or the Kalshi API/WebSocket.
run-simulations
by ratacatUse BEFORE and AFTER running trading engine simulations. Helps with: (1) SETUP - choosing configs, selecting segments via segment collections, batch sizing (recommend 2,000-3,000 runs); (2) EXECUTION - running batch simulations with --collection; (3) ANALYSIS - comprehensive diagnostics after runs. Triggers on: 'run simulations', 'test configs', 'batch simulation', 'analyze sim results', 'which configs to test', 'how many segments', 'simulation setup'.
every-style-editor-2
by ratacatUse this agent when you need to review and edit text content to conform to Every's specific style guide. This includes reviewing articles, blog posts, newsletters, documentation, or any written content that needs to follow Every's editorial standards. The agent will systematically check for title case in headlines, sentence case elsewhere, company singular/plural usage, overused words, passive voice, number formatting, punctuation rules, and other style guide requirements.
workflows-plan
by ratacatTransform feature descriptions into well-structured project plans following conventions
deepen-plan
by ratacatEnhance a plan with parallel research agents for each section to add depth, best practices, and implementation details
spec-flow-analyzer
by ratacatUse this agent when you have a specification, plan, feature description, or technical document that needs user flow analysis and gap identification. This agent should be used proactively when:\n\n<example>\nContext: The user has just finished drafting a specification for OAuth implementation.\nuser: "Here's the OAuth spec for our new integration:\n[OAuth spec details]"\nassistant: "Let me use the spec-flow-analyzer agent to analyze this OAuth specification for user flows and missing elements."\n<commentary>\nSince the user has provided a specification document, use the Task tool to launch the spec-flow-analyzer agent to identify all user flows, edge cases, and missing clarifications.\n</commentary>\n</example>\n\n<example>\nContext: The user is planning a new social sharing feature.\nuser: "I'm thinking we should add social sharing to posts. Users can share to Twitter, Facebook, and LinkedIn."\nassistant: "This sounds like a feature specification that would benefit from flow analysis. Let me use the spec-f...
ebook-extractor
by ratacatUse when user wants to extract text from ebooks (EPUB, MOBI, PDF). Use for converting ebooks to plain text for analysis, processing, or reading. Handles all common ebook formats.
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.