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...
litho-budgeted-analysis
by kivo360Profile a repository, inspect existing Litho/deepwiki-rs cache state, generate a repo-local Litho config that preserves the user's current global model/provider, and run deepwiki-rs within a target time budget. Use when Claude needs to analyze a repo quickly with Litho, auto-tune exclusions and boundary settings by repo size, reuse cache intelligently, or create a filtered local copy for very large repos.
oip-proposal
by kivo360Create OmoiOS Improvement Proposals (OIPs) through an interactive workflow. Gathers context from the user via clarifying questions, researches relevant codebase files, then writes a complete proposal following the OIP template. Use when requesting "create a proposal", "write an OIP", "new proposal", "propose a feature", "OIP", "improvement proposal", or when the user wants to formally propose a product, architecture, or process change for OmoiOS.
browser-use
by kivo360Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, or extract information from web pages.
billing-automation
by kivo360Build automated billing systems for recurring payments, invoicing, subscription lifecycle, and dunning management. Use when implementing subscription billing, automating invoicing, or managing recurring payment systems.
moai-lang-rust
by kivo360Rust best practices with systems programming, performance-critical applications, and memory-safe patterns for 2025
moai-lang-dart
by kivo360Dart best practices with Flutter mobile development, async programming, and server-side Dart for 2025
moai-lang-c
by kivo360C17/C23 best practices with Unity test framework, cppcheck, and Make/CMake build systems.
moai-alfred-personas
by kivo360Adaptive communication patterns and role selection based on user expertise level and request type (Consolidated from moai-alfred-persona-roles)
moai-foundation-trust
by kivo360Validates TRUST 5-principles (Test 85%+, Readable, Unified, Secured, Trackable). Use when aligning with TRUST governance.
oh-my-openagent
by kivo360Complete guide for oh-my-openagent (OMO) multi-model orchestration harness. Covers installation, agent selection, category system, cost optimization, and troubleshooting for OpenCode. Use when configuring OMO, setting up agents, optimizing costs, delegating to specialized models, or understanding the OMO agent team (Sisyphus, Hephaestus, Prometheus, Atlas, Oracle, etc.).
better-auth-ui
by kivo360Pre-built shadcn/ui authentication components for Better Auth. Use when implementing auth pages (sign in, sign up, forgot password, magic link), user buttons, account settings, organization management, or API key management with Better Auth. Trigger on "auth UI," "auth components," "sign in page," "sign up page," "user button," "settings page," "auth card," "AuthUIProvider," "better-auth-ui," "shadcn auth," "auth view," "organization switcher," "account settings," or "auth form." Package: @daveyplate/better-auth-ui. For backend auth config, see better-auth-complete. For testing auth UI, see dogfood-complete.
my-stack
by kivo360Master routing skill for the full SaaS stack: Next.js (next-forge) + Better Auth + Better Auth UI + Drizzle ORM + Stripe + Resend + React Email + Sentry + PostHog + shadcn/ui + Tailwind + Turborepo + Vercel + Playwright. Use for ANY task across the stack — routes to the right skills. Trigger on "my stack," "set up," "new project," "add feature," "scaffold," "deploy," "test," "integrate," "configure," "production," or any mention of the specific technologies. This is the top-level meta skill that coordinates 26 skills across auth, testing, infrastructure, and integrations.
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.