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...
shieldcn-badges
by jal-coAdd beautiful shadcn/ui-styled README badges to projects using shieldcn. Use when adding badges, shields, or status indicators to README files, docs, or markdown. Triggers include "add badges", "add shields", "readme badges", "npm badge", "GitHub stars badge", "CI badge", "shieldcn", or any request to add project status badges to documentation.
frontend-design
by jal-coCreate distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
vercel-composition-patterns
by jal-coReact composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
vercel-react-best-practices
by jal-coReact and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
components-build
by jal-coThe canonical standard for building React components. Covers composition, accessibility, typing, styling, state, polymorphism, asChild, data attributes, design tokens, and artifact taxonomy from the components.build specification by Hayden Bleasel and shadcn. Use for building any React component, reviewing component architecture, auditing accessibility and keyboard navigation, choosing between as/asChild, designing variant APIs with CVA, applying data-attribute-driven styling, classifying artifacts (primitive/component/block/pattern/template/utility), extending HTML types, and managing controlled/uncontrolled state. Use proactively when creating or reviewing React components, designing component APIs, building design systems, implementing keyboard navigation, adding ARIA support, structuring compound components, choosing element flexibility patterns, or applying Tailwind styling systems. Examples: - user: "Build an accessible dialog" → semantic HTML, focus trap, keyboard map, ARIA, asChild trigger - user: "Re
jalco-component-builder
by jal-coBuild jalco ui components through a deliberate workflow: clarify requirements, judge scope and file boundaries, prefer strong default states and restrained variants, implement with shadcn-style ergonomics, and ship aligned docs. Use when the user asks to create a component, build a component, make a new UI component, add a registry component, create a docs component, add variants, refactor a component, or review public components, demos, or docs-facing UI.
jalco-shadcn-registry
by jal-coBuild and maintain the jalco ui shadcn-compatible registry. Use when creating or reviewing registry items, editing registry.json, choosing registry item types, configuring namespaced registries, planning authentication, adding docs metadata, or ensuring MCP/open-in-v0 compatibility.
jalco-ui-registry
by jal-coCreate, review, and document jalco ui registry items with shadcn-style ergonomics, strong variants, and docs-to-registry alignment. Use for adding jalco ui components or blocks, updating registry.json, writing component docs, reviewing API and composition quality, or navigating the jalco-ui docs and registry structure. Use proactively when working in /Users/justin/Documents/Github/jalco-ui or when the user asks to create a component, improve variants, write docs, or audit registry readiness.
jalco-writing-component-docs
by jal-coWrite and review jalco ui component documentation with consistent structure, concise descriptions, realistic examples, and registry-aligned metadata. Use when creating new component docs, updating existing docs, reviewing doc quality, or syncing registry-backed component copy.
tailwind-design-system
by jal-coBuild scalable design systems with Tailwind CSS v4, design tokens, component libraries, and responsive patterns. Use when creating component libraries, implementing design systems, or standardizing UI patterns.
exa
by jal-coWeb search, content crawling, code context lookup, company research, and deep AI research via Exa. Use when the user needs to search the web, fetch page content from a URL, find code examples or documentation, research a company, or run a deep research report on a complex topic.
browser-tools
by jal-coInteractive browser automation via Chrome DevTools Protocol using Brave. Use when you need to interact with web pages, test frontends, or when user interaction with a visible browser is required.
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.