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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job-patterns
by ThibautBaissacImplements shallow background jobs with _later/_now conventions using Solid Queue. Use when adding background processing, async operations, scheduled tasks, or when user mentions jobs, queues, workers, or background processing. WHEN NOT: Business logic implementation (use model-patterns), controller work (use crud-patterns), or mailer delivery (use mailer-patterns).
security-audit
by ThibautBaissacAudits Rails application security against OWASP Top 10, detects vulnerabilities with Brakeman, and verifies Pundit authorization policies. Use when the user wants a security audit, vulnerability scan, or when user mentions security, OWASP, Brakeman, XSS, SQL injection, or authorization. WHEN NOT: Implementing security fixes (use specialist agents), setting up authentication (use authentication-flow), or writing Pundit policies (use policy-agent).
extraction-timing
by ThibautBaissacGuides decisions about when and how to extract code into services, queries, concerns, form objects, or other patterns. Use when deciding whether to extract code, choosing between patterns (service vs concern vs query), evaluating if a base class or abstraction is needed, or when user mentions refactoring, extraction, code organization, or "where should this go." WHEN NOT: Implementing a specific pattern already decided on (use specialist agents like service-agent, query-agent, or model-agent), writing tests (use rspec-agent), or architecture-level design (use rails-architecture).
caching-strategies
by ThibautBaissacImplements Rails caching patterns for performance optimization. Use when adding fragment caching, Russian doll caching, low-level caching, cache invalidation, or when user mentions caching, performance, cache keys, or memoization. WHEN NOT: General query optimization (use performance-optimization), background job processing, or problems caused by N+1 queries rather than missing caches.
caching-patterns
by ThibautBaissacImplements HTTP caching with ETags, fragment caching, Russian doll caching, and Solid Cache configuration. Use when optimizing performance, adding caching layers, or when user mentions ETags, fresh_when, stale?, cache keys, or Russian doll caching. WHEN NOT: For Turbo Stream real-time updates (use turbo-patterns), for background job cache warming logic (use job-patterns).
stimulus-patterns
by ThibautBaissacBuilds focused, single-purpose Stimulus controllers for progressive enhancement. Use when adding JavaScript behavior, UI interactions, form enhancements, or building reusable client-side components. WHEN NOT: For Turbo Stream/Frame patterns (see turbo-patterns skill). For server-side view logic (see rules/views.md).
turbo-patterns
by ThibautBaissacCreates Turbo Streams, Turbo Frames, and morphing patterns for real-time UI updates. Use when adding real-time updates, partial page rendering, form submissions, or broadcasting. WHEN NOT: For Stimulus JavaScript controllers (see stimulus-patterns skill). For general view conventions (see rules/views.md).
solid-queue-setup
by ThibautBaissacConfigures Solid Queue for background jobs in Rails 8. Use when setting up background processing, creating background jobs, configuring job queues, or migrating from Sidekiq to Solid Queue. WHEN NOT: Synchronous in-request processing, real-time WebSocket features (use Action Cable), or simple operations that don't need background execution.
rails-architecture
by ThibautBaissacGuides modern Rails 8 code architecture decisions and patterns. Use when deciding where to put code, choosing between patterns (service objects vs concerns vs query objects), designing feature architecture, refactoring for better organization, or when user mentions architecture, code organization, design patterns, or layered design. WHEN NOT: Implementing specific patterns (use specialist agents like service-agent or query-agent), writing tests, or debugging runtime errors.
rails-concern
by ThibautBaissacCreates Rails concerns for shared behavior across models or controllers with TDD. Use when extracting shared code, creating reusable modules, DRYing up models/controllers, or when user mentions concerns, modules, mixins, or shared behavior. WHEN NOT: Logic used by only one model or controller (keep it in place), complex business logic (use service objects), or query encapsulation (use query objects).
api-patterns
by ThibautBaissacBuilds REST APIs using respond_to blocks with Jbuilder templates following the 37signals same-controllers-different-formats philosophy. Use when adding API endpoints, JSON responses, token authentication, pagination, or when user mentions API, JSON, REST, or Jbuilder. WHEN NOT: For HTML-only controllers (use crud-patterns), for webhook delivery (use event-tracking).
auth-setup
by ThibautBaissacImplements custom passwordless authentication without Devise. Use when setting up authentication, login flows, session management, passkeys (WebAuthn), magic links, or password resets. Passkeys are the primary auth method; magic links are the fallback. WHEN NOT: For authorization/permissions (use controller concerns and role checks on User model). For multi-tenancy account scoping (see multi-tenant-setup skill).
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.