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.
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security-sentinel
by i3ringitUse this agent when you need to perform security audits, vulnerability assessments, or security reviews of code. This includes checking for common security vulnerabilities, validating input handling, reviewing authentication/authorization implementations, scanning for hardcoded secrets, and ensuring OWASP compliance. <example>Context: The user wants to ensure their newly implemented API endpoints are secure before deployment.\nuser: "I've just finished implementing the user authentication endpoints. Can you check them for security issues?"\nassistant: "I'll use the security-sentinel agent to perform a comprehensive security review of your authentication endpoints."\n<commentary>Since the user is asking for a security review of authentication code, use the security-sentinel agent to scan for vulnerabilities and ensure secure implementation.</commentary></example> <example>Context: The user is concerned about potential SQL injection vulnerabilities in their database queries.\nuser: "I'm worried about SQL inject
lint
by i3ringitUse this agent when you need to run linting and code quality checks on Ruby and ERB files. Run before pushing to origin.
pattern-recognition-specialist
by i3ringitUse this agent when you need to analyze code for design patterns, anti-patterns, naming conventions, and code duplication. This agent excels at identifying architectural patterns, detecting code smells, and ensuring consistency across the codebase. <example>Context: The user wants to analyze their codebase for patterns and potential issues.\nuser: "Can you check our codebase for design patterns and anti-patterns?"\nassistant: "I'll use the pattern-recognition-specialist agent to analyze your codebase for patterns, anti-patterns, and code quality issues."\n<commentary>Since the user is asking for pattern analysis and code quality review, use the Task tool to launch the pattern-recognition-specialist agent.</commentary></example><example>Context: After implementing a new feature, the user wants to ensure it follows established patterns.\nuser: "I just added a new service layer. Can we check if it follows our existing patterns?"\nassistant: "Let me use the pattern-recognition-specialist agent to analyze the new
performance-oracle
by i3ringitUse this agent when you need to analyze code for performance issues, optimize algorithms, identify bottlenecks, or ensure scalability. This includes reviewing database queries, memory usage, caching strategies, and overall system performance. The agent should be invoked after implementing features or when performance concerns arise.\n\n<example>\nContext: The user has just implemented a new feature that processes user data.\nuser: "I've implemented the user analytics feature. Can you check if it will scale?"\nassistant: "I'll use the performance-oracle agent to analyze the scalability and performance characteristics of your implementation."\n<commentary>\nSince the user is concerned about scalability, use the Task tool to launch the performance-oracle agent to analyze the code for performance issues.\n</commentary>\n</example>\n\n<example>\nContext: The user is experiencing slow API responses.\nuser: "The API endpoint for fetching reports is taking over 2 seconds to respond"\nassistant: "Let me invoke the per
pr-comment-resolver
by i3ringitUse this agent when you need to address comments on pull requests or code reviews by making the requested changes and reporting back on the resolution. This agent handles the full workflow of understanding the comment, implementing the fix, and providing a clear summary of what was done. <example>Context: A reviewer has left a comment on a pull request asking for a specific change to be made.user: "The reviewer commented that we should add error handling to the payment processing method"assistant: "I'll use the pr-comment-resolver agent to address this comment by implementing the error handling and reporting back"<commentary>Since there's a PR comment that needs to be addressed with code changes, use the pr-comment-resolver agent to handle the implementation and resolution.</commentary></example><example>Context: Multiple code review comments need to be addressed systematically.user: "Can you fix the issues mentioned in the code review? They want better variable names and to extract the validation logic"assis
repo-research-analyst
by i3ringitUse this agent when you need to conduct thorough research on a repository's structure, documentation, and patterns. This includes analyzing architecture files, examining GitHub issues for patterns, reviewing contribution guidelines, checking for templates, and searching codebases for implementation patterns. The agent excels at gathering comprehensive information about a project's conventions and best practices.\n\nExamples:\n- <example>\n Context: User wants to understand a new repository's structure and conventions before contributing.\n user: "I need to understand how this project is organized and what patterns they use"\n assistant: "I'll use the repo-research-analyst agent to conduct a thorough analysis of the repository structure and patterns."\n <commentary>\n Since the user needs comprehensive repository research, use the repo-research-analyst agent to examine all aspects of the project.\n </commentary>\n</example>\n- <example>\n Context: User is preparing to create a GitHub issue and wants to
skill-creator
by i3ringitGuide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Antigravity's capabilities with specialized knowledge, workflows, or tool integrations.
spec-flow-analyzer
by i3ringitUse 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-flow
agent-native-architecture
by i3ringitBuild applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
agent-native-reviewer
by i3ringitUse this agent when reviewing code to ensure features are agent-native - that any action a user can take, an agent can also take, and anything a user can see, an agent can see. This enforces the principle that agents should have parity with users in capability and context. <example>Context: The user added a new feature to their application.\nuser: "I just implemented a new email filtering feature"\nassistant: "I'll use the agent-native-reviewer to verify this feature is accessible to agents"\n<commentary>New features need agent-native review to ensure agents can also filter emails, not just humans through UI.</commentary></example><example>Context: The user created a new UI workflow.\nuser: "I added a multi-step wizard for creating reports"\nassistant: "Let me check if this workflow is agent-native using the agent-native-reviewer"\n<commentary>UI workflows often miss agent accessibility - the reviewer checks for API/tool equivalents.</commentary></example>
ankane-readme-writer
by i3ringitUse this agent when you need to create or update README files following the Ankane-style template for Ruby gems. This includes writing concise documentation with imperative voice, keeping sentences under 15 words, organizing sections in the standard order (Installation, Quick Start, Usage, etc.), and ensuring proper formatting with single-purpose code fences and minimal prose. Examples: <example>Context: User is creating documentation for a new Ruby gem. user: "I need to write a README for my new search gem called 'turbo-search'" assistant: "I'll use the ankane-readme-writer agent to create a properly formatted README following the Ankane style guide" <commentary>Since the user needs a README for a Ruby gem and wants to follow best practices, use the ankane-readme-writer agent to ensure it follows the Ankane template structure.</commentary></example> <example>Context: User has an existing README that needs to be reformatted. user: "Can you update my gem's README to follow the Ankane style?" assistant: "Let me
architecture-strategist
by i3ringitUse this agent when you need to analyze code changes from an architectural perspective, evaluate system design decisions, or ensure that modifications align with established architectural patterns. This includes reviewing pull requests for architectural compliance, assessing the impact of new features on system structure, or validating that changes maintain proper component boundaries and design principles. <example>Context: The user wants to review recent code changes for architectural compliance.\nuser: "I just refactored the authentication service to use a new pattern"\nassistant: "I'll use the architecture-strategist agent to review these changes from an architectural perspective"\n<commentary>Since the user has made structural changes to a service, use the architecture-strategist agent to ensure the refactoring aligns with system architecture.</commentary></example><example>Context: The user is adding a new microservice to the system.\nuser: "I've added a new notification service that integrates with our
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.