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.
Querying local SQLite index...
zoonk-design
by zoonkDesign philosophy and UI/UX guidelines inspired by Apple, Linear, and Vercel. Use when planning new features, designing interfaces, reviewing implementations, or making visual and interaction design decisions.
zoonk-translations
by zoonkWork with internationalization using next-intl. Use when handling translations, i18n, getTranslations, useTranslations, translating content, or when the user needs to add or modify translations.
zoonk-business
by zoonkBusiness decision-making framework for AI agents. Use when making strategic decisions, evaluating trade-offs, or ensuring alignment with Zoonk's mission and values.
zoonk-commit
by zoonkGuidelines for writing commit messages and PR descriptions. Use when creating commits, writing PR descriptions, or asking about commit format.
zoonk-compound-components
by zoonkBuild UI components using the compound component pattern. Use when creating new React components, building UI elements, refactoring components, or when the user mentions compound components, composable components, component patterns, or UI building blocks.
zoonk-github-issues
by zoonkCreate and manage GitHub issues with advanced features: issue types, dependencies, and sub-issues. Use this skill after planning issues with zoonk-issue-planning.
zoonk-issue-planning
by zoonkBreak down implementation plans into small, manageable GitHub issues. Use when you have a plan and want to create GitHub issues instead of implementing immediately. Outputs a structured breakdown with epic, sub-issues, and dependencies for review.
zoonk-testing
by zoonkWrite tests following TDD principles. Use when implementing features, fixing bugs, or adding test coverage. Covers e2e, integration, and unit testing patterns.
next-cache-components
by zoonkNext.js 16 Cache Components - PPR, use cache directive, cacheLife, cacheTag, updateTag
next-best-practices
by zoonkNext.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
android-material-guidelines
by zoonkUse when designing, reviewing, or implementing Android UI or features for phones, tablets, foldables, ChromeOS, desktop windowing, Wear OS, Android TV, Android for Cars, Android XR, widgets, Jetpack Compose, Material 3, Material Design, adaptive layouts, dynamic color, icons, dark theme, edge-to-edge UI, accessibility, or when porting web or iOS features to Android. Ensures current official Android Developers and Material Design guidance is checked and Android conventions are preferred over copying other platforms.
apple-human-interface-guidelines
by zoonkUse when designing, reviewing, or implementing any Apple-platform UI or feature for iOS, iPadOS, macOS, visionOS, tvOS, watchOS, SwiftUI, UIKit, AppKit, WatchKit, app icons, Dark Mode, SF Symbols, system colors, native controls, or when porting web features to Apple platforms. Ensures current official Apple Human Interface Guidelines are checked and platform conventions are preferred over copying web layouts.
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.