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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utopia-grid-layout
by matthewharwoodCSS Grid, Flexbox, and Subgrid using Utopia.fyi fluid spacing for gaps and gutters. Use when creating two-dimensional grid layouts, one-dimensional flex layouts, nested subgrids, or implementing the Utopian declarative grid system. Combines layout primitives with fluid space scales for proportional, responsive layouts without breakpoints.
rust-testing-verification
by matthewharwoodComprehensive testing strategies for Rust including property-based testing with proptest, fuzz testing with cargo-fuzz, benchmark testing with Criterion, contract testing for traits, and Miri for undefined behavior detection. Use when writing tests, ensuring code correctness, detecting edge cases, or measuring performance.
rust-style-conventions
by matthewharwoodFoundational Rust style conventions that apply universally to all Rust code. Covers import organization, naming conventions, and code formatting standards. This skill is automatically applied to all Rust implementations and should be referenced by all other Rust skills.
rust-production-reliability
by matthewharwoodProduction reliability patterns including circuit breakers with exponential backoff, graceful shutdown management with signal handling, retry logic with jitter, rate limiting with token bucket, and security best practices. Use when hardening services for production, implementing fault tolerance, adding retry logic, or ensuring graceful degradation.
rust-observability
by matthewharwoodProduction observability with tracing, OpenTelemetry, and Prometheus metrics including structured logging, instrumented functions, distributed tracing, health checks, and request correlation. Use when adding logging, metrics, tracing, or health endpoints to Rust services.
rust-error-handling
by matthewharwoodProduction error patterns with thiserror and anyhow, including error classification, HTTP/gRPC protocol mappings, context chains, retry logic, and testing. Use when designing error types for libraries or applications, mapping errors to API responses, or implementing retry mechanisms.
rust-core-patterns
by matthewharwoodProduction-ready Rust patterns for type-safe domain modeling including newtypes, type states, builders, smart constructors, and const generics. Use when creating domain primitives (IDs, emails), state machines, builders, or enforcing compile-time invariants in Rust code.
rust-async-runtime
by matthewharwoodProduction patterns for Tokio 1.48.0 async runtime including task spawning, channels (mpsc, oneshot, broadcast, watch), synchronization primitives, graceful shutdown, and lifecycle management. Use when spawning background tasks, managing concurrency, implementing graceful shutdown, or coordinating async work.
maud-syntax-fundamentals
by matthewharwoodCompile-time HTML templating with Maud using the html! macro for type-safe markup generation. Covers syntax patterns including elements, attributes, classes, IDs, content splicing, toggles, control flow (if/match/for), and DOCTYPE. Use when generating HTML in Rust, creating templates, or building server-side rendered pages.
axum-web-framework
by matthewharwoodProduction patterns for Axum 0.8.x HTTP services with Tower middleware, type-safe routing, state management with FromRef, extractors, and error handling. Use when building REST APIs, HTTP services, web applications, or adding middleware to Axum routers.
maud-components-patterns
by matthewharwoodReusable component patterns for Maud including the Render trait, function components, parameterized components, layout composition, partials, and component organization. Use when building reusable UI elements, creating component libraries, structuring templates, or implementing design systems with type-safe components.
maud-axum-integration
by matthewharwoodProduction patterns for integrating Maud HTML templates with Axum 0.8.x web services. Covers IntoResponse implementation, handler patterns, state management with templates, error pages, layouts, and server-side rendering architecture. Use when building Axum HTTP endpoints that return HTML, creating web UIs, or implementing server-side rendered applications.
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.