Related Pages

Related topics: Quick Start Guide, Compatible Agents

Project Introduction

Overview

awesome-agent-skills is a curated, community-maintained collection of AI agent skills designed to extend the capabilities of AI assistants across multiple platforms. The repository serves as a central directory where developers and users can discover, share, and deploy reusable skill packages for various AI agent implementations.

Sources: website/index.html

What Are Agent Skills?

Agent Skills are portable instruction files that teach AI assistants how to perform specific tasks. Think of them as loadable expertise: domain knowledge, team conventions, and repeatable workflows that your agent can access exactly when needed.

The skill format was originally developed by Anthropic and released as an open standard. It has since been adopted by over 30 agent products, making skills a truly cross-platform solution for extending AI capabilities.

Sources: website/index.html

Related Pages

Related topics: Project Introduction, Using and Creating Skills

Quick Start Guide

Overview

The Agent Skill Index is a curated collection of AI agent skills—portable instruction files that teach AI assistants how to perform specific tasks. Skills are designed as SKILL.md files containing YAML frontmatter and plain-English instructions that agents can load on demand.

The repository serves three primary audiences:

Sources: website/index.html

Three-Step Workflow

Getting started with Agent Skills follows a simple three-step process:

graph TD
    A[Find a Skill] --> B[Load into AI]
    B --> C[Ask in Plain English]

Step 1: Find a Skill

Browse the skills directory on the website. Skills are organized by category including development, data, automation, and security. Each card links directly to the skill on GitHub.

Sources: website/index.html

Step 2: Load It Into Your AI

Copy the GitHub URL and add it to your AI agent. Each platform supports different methods:

Alternatively, you can manually place skill folders into supported directories:

.github/skills/skill-name/

Sources: website/src/components/sections/UsingSkills.tsx

Step 3: Ask in Plain English

Simply tell your AI what you want. The agent reads the skill instructions automatically and executes accordingly. No commands to memorize, no configuration required.

Sources: website/index.html

SKILL.md Format

Every skill is a folder containing a SKILL.md file. The file uses YAML frontmatter followed by plain Markdown content.

Required Frontmatter Fields

Optional Frontmatter Fields

Recommended Body Structure

The Markdown body is free-form, but the documentation recommends these sections:

## When to use this skill
Trigger conditions, scope, and limitations.

## Instructions
Step-by-step procedure. Be explicit.

## Gotchas
Environment-specific facts. Things that defy expectations.

## Examples
Concrete input → output pairs.

## Validation
How to verify the output is correct.

Sources: website/index.html

Minimal Example

Related Pages

Related topics: Project Introduction, Skill Directories

Compatible Agents

Agent Skills is an open standard that has been adopted by leading AI development tools and platforms. This document provides a comprehensive overview of the compatible agents ecosystem, explaining how skills work across different platforms, the supported agent list, and how to leverage cross-platform compatibility.

Overview

Agent Skills were originally developed by Anthropic as an open standard for packaging and distributing reusable instructions for AI assistants. The format was designed with portability as a core principle — skills created for one agent platform can work seamlessly on any other platform that implements the specification.

The ecosystem now includes 30+ agent products that support the SKILL.md format. This widespread adoption means that skill authors can write instructions once and deploy them across multiple platforms without modification, while users can share and discover skills that work with their preferred AI tools.

Sources: website/index.html

How Skills Work Across Platforms

The Agent Skills system uses a lightweight discovery mechanism that enables cross-platform compatibility. At startup, agents scan configured directories and load only the name and description fields from each skill's SKILL.md file — approximately 50–100 tokens per skill. This keeps startup performance fast while allowing the agent to know what capabilities are available.

When a user's task matches a skill's description, the agent loads the complete SKILL.md file into context, executing the instructions and using any provided scripts. This on-demand loading pattern ensures that skills are only consumed when relevant, maintaining efficient resource usage.

graph TD
    A[Agent Startup] --> B[Scan Skills Directories]
    B --> C[Load name + description only]
    C --> D[Skill Discovery Complete]
    D --> E[User Request]
    E --> F{Does request match skill?}
    F -->|Yes| G[Load Full SKILL.md]
    F -->|No| H[Process without skill]
    G --> I[Execute Instructions]
    H --> J[Return Response]
    I --> J

Sources: website/index.html

Supported Agent Platforms

The following table lists the major agent platforms that support the Agent Skills standard:

Sources: README.ko.md

Complete Platform List

The ecosystem includes over 30 platforms that have adopted the Agent Skills standard. Based on the website documentation, the full list of supported platforms includes:

Sources: website/index.html

Skills Installation Methods by Platform

Different agent platforms support various methods for adding and managing skills. Understanding these installation methods is essential for effectively using the skill ecosystem.

Claude Code CLI

For Claude Code users, skills can be added directly through the command-line interface:

/claude/skills add <github-url>

Claude.ai Web Interface

Claude.ai users can paste the raw SKILL.md URL directly into their chat. The agent automatically detects and loads skill instructions when relevant context is provided.

Manual File Placement

For development workflows and offline scenarios, skills can be manually placed in platform-specific directories:

Sources: website/index.html

npx skills CLI Tool

The npx skills command-line tool provides a unified interface for skill discovery, addition, and management across supported platforms:

Sources: README.ko.md

The tool supports multiple input formats:

• GitHub shorthand: owner/repo
• Full URLs: https://github.com/owner/repo
• Local paths: Relative or absolute file paths

Cross-Platform Compatibility Architecture

The Agent Skills specification defines a standard format that ensures compatibility across all implementing platforms. This architecture consists of several layers:

graph TD
    A[SKILL.md File] --> B[Frontmatter Metadata]
    A --> C[Markdown Body]
    B --> D[name field]
    B --> E[description field]
    B --> F[optional fields]
    C --> G[Instructions]
    C --> H[Examples]
    C --> I[Gotchas]
    C --> J[Validation steps]
    F --> K[license]
    F --> L[compatibility]
    F --> M[allowed-tools]

Required Frontmatter Fields

Every SKILL.md file must include the following YAML frontmatter:

Optional Frontmatter Fields

Sources: website/index.html

Skill Directory Structure

Agent platforms scan specific directories to discover available skills. The standard directory structure ensures consistent behavior across all supported platforms:

.claude/skills/          # Claude Code and Claude.ai
.github/skills/          # GitHub-integrated agents
.agents/skills/          # General agent platforms
~/.agents/skills/        # User-level skills (user home directory)

Within each skills directory, skills are organized in subdirectories with the following structure:

<skill-name>/
└── SKILL.md            # Required: Contains frontmatter + instructions
scripts/                # Optional: Executable scripts the skill can run

Sources: website/index.html

Platform-Specific Considerations

Claude Code

Claude Code provides native support for skills through the /skills command:

/claude/skills add <github-url>

Skills installed via Claude Code are automatically scanned at startup and loaded when relevant.

Claude.ai Web

Claude.ai users can utilize skills by:

1. Copying a skill's GitHub URL
2. Pasting the raw SKILL.md URL into the chat

The web interface automatically detects skill format and applies instructions as needed.

Third-Party Platforms

For platforms like Codex, Copilot, Cursor, and Gemini CLI, the specific skill loading mechanisms may vary. Users should consult individual platform documentation for detailed instructions on skill installation and usage.

Sources: website/index.html

Skill Discovery and Management

The skill ecosystem supports multiple discovery mechanisms to help users find relevant skills:

Direct URL Addition

Users can directly add skills by providing GitHub repository URLs or shorthand notation. The agent resolves the URL, fetches the SKILL.md file, and installs it in the appropriate skills directory.

CLI-Based Discovery

The npx skills find [query] command enables keyword-based search across registered skills. This is particularly useful for discovering skills that address specific use cases or domains.

Manual Installation

For offline environments or custom workflows, users can manually download skill folders and place them in the appropriate directory:

# Example: Manual skill installation
cp -r /path/to/downloaded-skill \
  ~/.agents/skills/my-skill-name

Sources: README.ko.md

Verification and Validation

The Agent Skills ecosystem includes validation mechanisms to ensure skill quality and compatibility:

Users can verify that a skill is correctly installed by checking for the presence of its SKILL.md file:

ls ~/.agents/skills/my-skill/SKILL.md

Sources: README.ko.md

Related Resources

For more information about compatible agents and the Agent Skills ecosystem, refer to:

• Official Specification — Complete SKILL.md format specification
• Creating Skills Guide — Tutorial for skill authors
• Using Skills in Claude — Claude.ai user guide

Related Pages

Related topics: Component Architecture, Deployment Configuration

Next.js Website Structure

Overview

The awesome-agent-skills repository includes a Next.js-based website (/website) that serves as the public-facing documentation and showcase for agent skills. This web application is built using Next.js 14+ with the App Router paradigm, TypeScript for type safety, and Tailwind CSS for styling. The site provides an interactive interface for exploring agent skills, understanding how they work, and navigating through comprehensive documentation.

The website architecture follows a component-based modular design where reusable UI sections are organized in /src/components/sections/, while shared utilities like internationalization (i18n) live in /src/lib/. The application uses the Next.js App Router for routing and leverages server-side rendering capabilities for optimal performance and SEO. Sources: website/src/lib/i18n.tsx

Project Architecture

Directory Structure

The website follows a standard Next.js App Router structure:

website/
├── src/
│   ├── app/                    # App Router pages and layouts
│   ├── components/            # Reusable React components
│   │   ├── sections/          # Page section components
│   │   ├── Footer.tsx         # Global footer component
│   │   └── WikiSidebar.tsx    # Navigation sidebar
│   └── lib/                   # Utility functions
│       └── i18n.tsx           # Internationalization setup
├── public/                    # Static assets
├── next.config.ts             # Next.js configuration
└── tsconfig.json              # TypeScript configuration

Technology Stack

The project utilizes CSS variables for theming, supporting both light and dark modes through Tailwind's dark mode variants. The application uses next/font for automatic font optimization, loading Geist font family for consistent typography across the site. Sources: website/README.md

Component Architecture

Section Components

The website is composed of modular section components located in /src/components/sections/. Each section represents a distinct portion of the page content:

#### Section Component Pattern

Each section component follows a consistent pattern using the "use client" directive for client-side interactivity:

"use client";

import { useTranslations } from "@/lib/i18n";

export default function SectionName() {
  const t = useTranslations();
  
  return (
    <section id="section-id" className="scroll-mt-20 py-16 border-b ...">
      <h2 className="...">{t.section.title}</h2>
      {/* Section content */}
    </section>
  );
}

Sources: website/src/components/sections/HowItWorks.tsx

Navigation Components

#### WikiSidebar.tsx

The WikiSidebar component provides hierarchical navigation within the documentation. It renders a collapsible sidebar with grouped navigation items and supports visual indicators for new content.

Key features include:

• Hierarchical category grouping
• "New" badge support for recent additions
• External link indicators
• Dark mode compatible styling
• Animated transitions using framer-motion

The sidebar fetches navigation data from the i18n configuration and renders links with appropriate styling based on their type (internal or external). Sources: website/src/components/WikiSidebar.tsx

#### Footer.tsx

The global footer component provides site-wide information and external links:

• Site branding and copyright information
• Social media links (GitHub, X/Twitter)
• Contact email link
• BibTeX citation format for academic references
• Call-to-action buttons for main resources

export default function Footer() {
  return (
    <footer className="...">
      {/* BibTeX citation block */}
      {/* Social links: GitHub, Twitter, Email */}
      {/* External resource buttons */}
    </footer>
  );
}

Sources: website/src/components/Footer.tsx

Internationalization (i18n)

i18n Implementation

The website implements internationalization using a custom i18n solution defined in /src/lib/i18n.tsx. The system provides translations for multiple languages including English, Japanese, and presumably Chinese based on the repository's focus.

#### Translation Structure

Translations are organized hierarchically:

export const translations = {
  nav: {
    title: "Navigation",
    items: ["Skills", "Directory", "Standards", "Guide", "Trends", "FAQ"]
  },
  resources: {
    title: "Resources",
    items: [
      { label: "agent-skill.co", href: "https://agent-skill.co" },
      { label: "GitHub Repository", href: "..." }
    ]
  },
  how: {
    title: "How it works",
    subtitle: "...",
    steps: [
      { step: "01", title: "...", desc: "..." }
    ]
  }
  // Additional translation keys...
};

#### Translation Hook

Components access translations through the useTranslations() hook:

const t = useTranslations();
// Access nested properties
t.nav.title        // Returns "Navigation"
t.how.steps        // Returns array of step objects

Sources: website/src/lib/i18n.tsx

Supported Languages

Based on the i18n file content, the website supports:

Styling and Theming

Tailwind CSS Integration

The website uses Tailwind CSS with the following configuration:

• Dark mode support via dark: class variants
• Custom color palette using neutral grays
• Responsive design breakpoints (md, lg)
• Custom scroll margin for anchor navigation (scroll-mt-20)

Color System

The styling uses a neutral color system defined through Tailwind classes:

Component Styling Patterns

#### Card Components

Section cards use consistent styling patterns:

<div className="p-6 border border-neutral-200 dark:border-neutral-800 rounded-xl bg-white dark:bg-neutral-900">
  <h3 className="text-base font-semibold ...">{title}</h3>
  <p className="text-sm text-neutral-500 ...">{description}</p>
</div>

#### Table Components

Data tables follow a consistent structure:

<table className="w-full text-sm">
  <thead>
    <tr className="bg-neutral-50 dark:bg-neutral-800 border-b ...">
      <th className="text-left px-5 py-3 text-xs ...">Column</th>
    </tr>
  </thead>
  <tbody className="divide-y divide-neutral-100 ...">
    {/* Rows */}
  </tbody>
</table>

Sources: website/src/components/sections/QualityStandards.tsx, website/src/components/sections/CompatibleAgents.tsx

Page Sections and Content Flow

Main Page Structure

The primary page (/app/page.tsx) renders sections in a specific order:

graph TD
    A[Hero Section] --> B[How It Works]
    B --> C[Quality Standards]
    C --> D[Compatible Agents]
    D --> E[Finding Skills]
    E --> F[Using Skills]
    F --> G[Creating Skills]
    G --> H[Skill Directory]
    H --> I[Tutorials]
    I --> J[Contributing]
    J --> K[FAQ]
    K --> L[Footer]

Section Details

#### How It Works Section

Explains the three-step process for agent skills:

1. Find - Discover skills in the directory
2. Add - Add skills to your AI tool
3. Use - Agent automatically applies the skill

Each step is displayed in a card with a large step number, title, and description. Sources: website/src/components/sections/HowItWorks.tsx

#### Quality Standards Section

Defines what makes a good agent skill:

• Good vs Bad Pattern Comparison: Side-by-side code examples
• Pattern Validation: Shows recommended vs discouraged practices
• Icon Indicators: CheckCircle2 for good, XCircle for bad

// Good pattern
<p className="text-xs font-mono text-neutral-600 ...">
  {t.quality.goodPattern}
</p>

// Bad pattern  
<p className="text-xs font-mono text-neutral-400 ... opacity-70">
  {t.quality.badPattern}
</p>

Sources: website/src/components/sections/QualityStandards.tsx

#### Skill Directory Section

The interactive skills catalog features:

• Framer Motion Integration: Animated grid with layout animations
• Filtering: Client-side filtering by skill category
• Card Design: Each skill displays name, icon, and external link
• Responsive Grid: grid-cols-1 md:grid-cols-2 for mobile to desktop

<motion.div layout className="grid grid-cols-1 md:grid-cols-2 gap-3">
  <AnimatePresence mode="popLayout">
    {filtered.map((skill) => (
      <motion.a
        layout
        key={skill.id}
        initial={{ opacity: 0, scale: 0.98 }}
        animate={{ opacity: 1, scale: 1 }}
        exit={{ opacity: 0, scale: 0.98 }}
        transition={{ duration: 0.15 }}
      >
        {/* Skill card content */}
      </motion.a>
    ))}
  </AnimatePresence>
</motion.div>

Sources: website/src/components/sections/SkillDirectory.tsx

Interactive Features

Code Copy Functionality

The UsingSkills.tsx component includes copy-to-clipboard functionality:

const [copied, setCopied] = useState(false);

const copyCode = async () => {
  await navigator.clipboard.writeText(code);
  setCopied(true);
  setTimeout(() => setCopied(false), 2000);
};

return (
  <button onClick={copyCode}>
    {copied ? <Check className="..." /> : <Copy className="..." />}
  </button>
);

Sources: website/src/components/sections/UsingSkills.tsx

Active Navigation Highlighting

The website implements scroll-based active navigation using the Intersection Observer API:

const navLinks = document.querySelectorAll('.nav-link');
const sections = ['newcomer', 'tutorial', 'reference', 'dir-section', 'agents', 'resources'];

const observer = new IntersectionObserver(entries => {
  for (const e of entries) {
    if (e.isIntersecting) {
      const id = e.target.id;
      navLinks.forEach(l => {
        l.classList.toggle('active', l.getAttribute('href') === `#${id}`);
      });
    }
  }
}, { threshold: 0.2, rootMargin: '-52px 0px 0px 0px' });

This provides visual feedback as users scroll through different page sections. Sources: website/index.html

Build and Deployment

Development Workflow

npm run dev    # Start development server
yarn dev       # Alternative with Yarn
pnpm dev       # Alternative with pnpm
bun dev        # Alternative with Bun

Font Optimization

The project uses next/font for automatic font optimization:

// In layout.tsx
import { Geist } from "next/font/google";

// Font configuration is handled automatically

This loads the Geist font family and optimizes it for performance, reducing layout shift during page loads.

Production Build

The Next.js application can be deployed to Vercel or any Node.js-compatible hosting platform. The standard build process:

npm run build   # Create production build
npm start       # Start production server

Sources: website/README.md

Summary

The Next.js website structure for awesome-agent-skills demonstrates a well-organized, component-based architecture suitable for documentation and showcase sites. Key architectural decisions include:

• Modular Sections: Each page section is a self-contained component
• i18n System: Custom translation hook with hierarchical structure
• Theming: Dark mode support through Tailwind CSS
• Animations: Framer Motion for smooth transitions
• Accessibility: Semantic HTML with proper ARIA attributes
• Performance: Next.js App Router with automatic font optimization

The architecture prioritizes maintainability and extensibility, making it straightforward to add new sections or modify existing ones. The separation of concerns between presentation components and utility functions (i18n) ensures clean code organization as the project scales.

Related Pages

Related topics: Next.js Website Structure, Internationalization (i18n)

Component Architecture

Overview

The awesome-agent-skills website is a Next.js-based documentation and showcase platform built with React and TypeScript. The component architecture follows a modular, section-driven design pattern where reusable UI components are composed into distinct page sections. The project uses Tailwind CSS for styling with dark mode support and framer-motion for animations. Sources: website/src/components/sections/SkillDirectory.tsx:1-50

Directory Structure

website/src/components/
├── LayoutShell.tsx        # Main layout wrapper
├── Navbar.tsx             # Top navigation bar
├── Footer.tsx             # Page footer
├── WikiSidebar.tsx        # Documentation sidebar navigation
└── sections/
    ├── Hero.tsx            # Hero banner section
    ├── WhatIsIt.tsx        # Project introduction
    ├── SkillDirectory.tsx # Skills catalog grid
    ├── UsingSkills.tsx     # Usage instructions
    ├── CreatingSkills.tsx  # Skill creation guide
    ├── FindingSkills.tsx   # Skill discovery resources
    ├── QualityStandards.tsx # Good vs bad patterns
    ├── Tutorials.tsx        # Interactive tutorials
    └── Contributing.tsx    # Contribution guide

Component Hierarchy

graph TD
    A[LayoutShell] --> B[Navbar]
    A --> C[WikiSidebar]
    A --> D[Section Components]
    A --> E[Footer]
    
    D --> D1[WhatIsIt]
    D --> D2[SkillDirectory]
    D --> D3[UsingSkills]
    D --> D4[CreatingSkills]
    D --> D5[FindingSkills]
    D --> D6[QualityStandards]
    D --> D7[Tutorials]
    D --> D8[Contributing]
    
    B --> B1[LanguageSelector]
    B --> B2[ThemeToggle]
    B --> B3[GitHubStars]

Core Layout Components

LayoutShell

The LayoutShell component serves as the main application wrapper that orchestrates all other components. It integrates the Navbar, WikiSidebar, page sections, and Footer into a cohesive layout structure.

Navbar

The Navbar component provides top-level navigation and global controls. Key features include:

Sources: website/src/components/Navbar.tsx:1-40

The language selector uses framer-motion for smooth dropdown animations with the following states:

{langOpen && (
  <motion.div
    initial={{ opacity: 0, y: -4, scale: 0.97 }}
    animate={{ opacity: 1, y: 0, scale: 1 }}
    exit={{ opacity: 0, y: -4, scale: 0.97 }}
    transition={{ duration: 0.15 }}
    className="absolute right-0 top-10 w-44..."
  >
    {LANGUAGES.map((l) => (...))}
  </motion.div>
)}

WikiSidebar

The WikiSidebar component provides documentation navigation with hierarchical menu items. It supports:

• Collapsible menu sections with AnimatePresence
• "New" badge indicators using the Sparkles icon for newly added items
• External links to agent-skill.co and GitHub repository
• Smooth fade animations for menu state changes

Sources: website/src/components/WikiSidebar.tsx:1-60

Footer

The Footer component displays project metadata and social links:

<a href="https://agent-skill.co" ...>agent-skill.co</a>
<a href="https://github.com/heilcheng/awesome-agent-skills" ...><Github /></a>
<a href="https://x.com/haileyhmt" ...><Twitter /></a>
<a href="mailto:[email protected]" ...><Mail /></a>

Includes BibTeX citation format for academic references. Sources: website/src/components/Footer.tsx:1-30

Section Components

WhatIsIt

Introduces the project concept using icon-based cards. Each card displays a title, description, and corresponding icon in a responsive grid layout.

SkillDirectory

The SkillDirectory component renders a filterable grid of skills with the following structure:

<motion.a
  layout
  key={skill.id}
  href={`https://github.com/heilcheng/awesome-agent-skills`}
  target="_blank"
  rel="noopener noreferrer"
  className="group flex items-start gap-4 p-4 border..."
>
  <div className="p-2 rounded-lg bg-neutral-100 dark:bg-neutral-800">
    <skill.Icon className="w-4 h-4" />
  </div>
  <div className="flex-1 min-w-0">
    <h3 className="text-sm font-semibold">{skill.name}</h3>
    <p className="text-xs text-neutral-500">{skill.description}</p>
  </div>
  <ExternalLink className="w-3.5 h-3.5" />
</motion.a>

Sources: website/src/components/sections/SkillDirectory.tsx:1-50

UsingSkills

Demonstrates how to add skills to an agent with code examples:

Includes a copy-to-clipboard button that uses Check and Copy icons from Lucide React. Sources: website/src/components/sections/UsingSkills.tsx:1-40

CreatingSkills

Displays the SKILL.md blueprint structure for creating new skills. Shows section-by-section breakdown with descriptions in a card format.

{t.creating.blueprint.map((section, i) => (
  <div key={i} className="px-4 py-3">
    <div className="text-xs font-mono font-bold">{section.section}</div>
    <p className="text-xs text-neutral-500 italic">{section.desc}</p>
  </div>
))}

Sources: website/src/components/sections/CreatingSkills.tsx:1-30

QualityStandards

Shows good vs bad code patterns side-by-side with visual indicators:

Uses dark mode compatible styling with Tailwind's dark: modifier. Sources: website/src/components/sections/QualityStandards.tsx:1-50

FindingSkills

Lists external resources for discovering agent skills including marketplaces and CLI commands:

{cliCommands.map(({ cmd, desc }) => (
  <div className="flex items-center gap-4 px-4 py-2.5...">
    <code className="text-xs font-mono text-neutral-700">{cmd}</code>
    <span className="text-xs text-neutral-400">{desc}</span>
  </div>
))}

Supports conditional image rendering for marketplace and leaderboard sections. Sources: website/src/components/sections/FindingSkills.tsx:1-40

Tutorials

Implements chat-style tutorial dialogues with:

• Character avatars (emoji-based)
• Quest/step indicators with icons
• External links to documentation resources

Uses ArrowUpRight icon for external link indicators. Sources: website/src/components/sections/Tutorials.tsx:1-50

Contributing

Simple section with call-to-action buttons linking to:

• Contributing Guide (/CONTRIBUTING.md)
• GitHub Repository

Sources: website/src/components/sections/Contributing.tsx:1-30

Data Flow Architecture

graph LR
    A[i18n Translations] --> B[Section Components]
    C[Props/State] --> D[UI Rendering]
    B --> D
    E[External APIs] --> F[SkillDirectory Data]
    F --> D

Animation Strategy

The project uses framer-motion consistently across components:

Example from SkillDirectory:

<motion.a
  initial={{ opacity: 0, scale: 0.98 }}
  animate={{ opacity: 1, scale: 1 }}
  exit={{ opacity: 0, scale: 0.98 }}
  transition={{ duration: 0.15 }}
>

Dark Mode Support

All components implement dark mode using Tailwind's dark: modifier:

Dependencies

Key external dependencies for the component architecture:

State Management Pattern

Components receive data via props and manage local state using React hooks. No global state management library is used; instead, props drilling and localized state is preferred for simplicity.

Related Pages

Related topics: Compatible Agents, Skill Quality Standards

Skill Directories

Overview

A Skill Directory is the organizational structure that stores, categorizes, and makes AI agent skills discoverable. This repository serves as a curated collection of reusable skills compatible with 30+ agent platforms including Claude Code, Claude.ai, OpenAI Codex, GitHub Copilot, Cursor, and more.

The directory enables:

• Discovery: Browse and search skills by category
• Installation: Copy skills directly into your agent's skill folder
• Portability: Skills work across different agents without modification
• Version Control: Track changes and contributions via Git

Sources: website/index.html:1-50

Directory Structure

Skills in this repository follow a standardized folder structure. Each skill is contained within its own directory containing a mandatory SKILL.md file.

awesome-agent-skills/
├── .claude/skills/          # Skills compatible with Claude Code
├── .github/skills/          # Skills compatible with GitHub-based agents
└── website/
    └── src/components/
        └── sections/
            └── SkillDirectory.tsx  # Frontend component for browsing

Skill Package Structure

my-skill/
├── SKILL.md           # Required: skill metadata and instructions
├── LICENSE            # Optional: license file
└── scripts/           # Optional: executable helper scripts
    └── *.py
    └── *.sh

Sources: website/index.html:85-120

SKILL.md Format Specification

Every skill must contain a SKILL.md file with YAML frontmatter. Only name and description are required.

Required Fields

Optional Fields

Sources: website/index.html:180-220

Example SKILL.md

Related Pages

Related topics: Skill Directories, Using and Creating Skills

Skill Quality Standards

Overview

Skill Quality Standards define the criteria and best practices for creating effective, reliable, and maintainable Agent Skills within the awesome-agent-skills ecosystem. These standards ensure that skills are consistently structured, easily discoverable, and capable of delivering predictable behavior across different AI agents and platforms.

The quality framework establishes what constitutes a "good" skill versus a poorly constructed one, providing authors with clear guidelines for implementation. This system helps maintain ecosystem integrity while enabling contributors to build skills that genuinely enhance AI agent capabilities.

Purpose and Scope

The Skill Quality Standards system serves multiple stakeholders within the AI agent ecosystem:

• For Skill Authors: Provides concrete guidelines on how to structure skills for maximum effectiveness
• For AI Agents: Ensures consistent instruction formatting that agents can reliably parse and execute
• For End Users: Guarantees predictable, high-quality outcomes when deploying skills
• For Ecosystem Maintainers: Offers evaluation criteria for curating and reviewing community submissions

The scope encompasses all aspects of skill creation including file structure, instruction clarity, example quality, and validation mechanisms. Skills that meet these standards are more likely to function correctly across the 30+ supported agent platforms including Claude Code, Claude.ai, OpenAI Codex, GitHub Copilot, and Cursor.

Quality Evaluation Framework

Core Quality Dimensions

Skills are evaluated across five primary dimensions that collectively determine their overall quality and reliability.

Quality Assessment Process

The quality assessment follows a structured evaluation workflow that ensures consistent scoring across all submitted skills.

graph TD
    A[Skill Submission] --> B[Metadata Review]
    B --> C[Instruction Analysis]
    C --> D[Example Validation]
    D --> E[Edge Case Audit]
    E --> F[Final Quality Score]
    
    B --> B1{name matches dir?}
    B1 -->|No| G[Rejection: Name Mismatch]
    B1 -->|Yes| C
    
    C --> C1{Instructions clear?}
    C1 -->|Unclear| H[Return for Revision]
    C1 -->|Clear| D
    
    D --> D1{Examples valid?}
    D1 -->|Invalid| H
    D1 -->|Valid| E
    
    E --> E1{Gotchas documented?}
    E1 -->|Missing| I[Recommendation Added]
    E1 -->|Documented| F
    
    style G fill:#ff6b6b
    style H fill:#ffd93d
    style F fill:#6bcb77

Good vs. Bad Pattern Examples

The quality standards explicitly define what constitutes effective versus ineffective skill implementations through concrete pattern comparisons.

Pattern Comparison Table

Implementation Example

The quality standards component provides visual comparison panels that demonstrate these differences:

// QualityStandards.tsx structure
interface QualityStandard {
  title: string;
  description: string;
}

interface PatternComparison {
  goodHeader: string;
  badHeader: string;
  goodPattern: string;
  badPattern: string;
  goodDesc: string;
  badDesc: string;
}

The component renders two side-by-side panels:

• Left Panel (Good): Green-accented header with CheckCircle2 icon, displays the recommended pattern in monospace font, includes explanatory text below
• Right Panel (Bad): Muted header with XCircle icon, displays the anti-pattern with reduced opacity, includes descriptive critique

Skill Structure Requirements

Beyond instruction quality, the standards define structural requirements that skills must satisfy.

Directory Structure

skill-name/
├── SKILL.md           # Required: Main skill definition
├── scripts/           # Optional: Executable helper scripts
│   └── *.py, *.sh
├── LICENSE            # Optional: License specification
└── README.md          # Optional: Extended documentation

Frontmatter Requirements

Progressive Disclosure Model

The quality standards embrace a progressive disclosure architecture that optimizes for both initial discovery performance and comprehensive execution capability.

graph LR
    subgraph Discovery["Startup Phase - Lightweight Scan"]
        A1[Agent loads] --> A2[Read name]
        A2 --> A3[Read description]
        A3 --> A4[~50-100 tokens per skill]
    end
    
    subgraph Activation["On-Demand Loading"]
        B1[Task matches description?] -->|Yes| B2[Load full SKILL.md]
        B2 --> B3[Read instructions]
        B3 --> B4[Load examples]
        B4 --> B5[Load scripts if needed]
    end
    
    A4 -.->|Task relevance?| B1
    
    style Discovery fill:#e3f2fd
    style Activation fill:#fff3e0

This model ensures that agents remain responsive during startup while having access to complete instructions when a relevant task is detected.

Best Practices Summary

Based on the quality standards framework, skill authors should adhere to the following practices:

1. Write Descriptive Trigger Conditions: The description field should explicitly state when to activate the skill, including specific keywords users would employ

1. Structure Instructions Step-by-Step: Break procedures into numbered, sequential steps that agents can follow unambiguously

1. Document Edge Cases: Include a Gotchas section covering environment-specific behaviors, common failure modes, and known limitations

1. Provide Concrete Examples: Include input → output pairs that demonstrate expected behavior in realistic scenarios

1. Include Validation Steps: Define how to verify that the skill's output meets success criteria

1. Use Agent-Friendly Scripts: When including scripts, design them to avoid interactive prompts, produce structured output (JSON/CSV), and exit with meaningful codes

Integration with Skill Ecosystem

The quality standards integrate with the broader skill ecosystem through the following mechanisms:

The npx skills command provides validation through npx skills check, which verifies that skills conform to the defined standards and alerts authors to areas needing improvement.

References

• Quality Standards Component: website/src/components/sections/QualityStandards.tsx
• README Documentation: README.md
• Skill Specification: agentskills.io/specification

Related Pages

Related topics: Quick Start Guide, Skill Quality Standards

Using and Creating Skills

Agent Skills are portable instruction packages that teach AI assistants how to perform specific tasks. A skill consists of a SKILL.md file containing YAML frontmatter and markdown instructions, optionally bundled with executable scripts. Skills follow an open standard originally developed by Anthropic, now adopted by 30+ agent platforms including Claude, Codex, Cursor, Copilot, and Gemini CLI.

Overview

The SKILL.md format enables AI agents to load domain knowledge, team conventions, and repeatable workflows on demand. At startup, agents perform a lightweight scan of available skills, loading only the name and description fields (~50-100 tokens per skill). Full instructions are loaded only when a task matches a skill's description.

Skill Discovery Locations

Agents scan the following directories for available skills:

SKILL.md Format Specification

Required Frontmatter Fields

Related Pages

Related topics: Component Architecture, Next.js Website Structure

Internationalization (i18n)

Overview

The awesome-agent-skills project implements a comprehensive internationalization system that enables the website and documentation to be presented in multiple languages. The i18n system supports six languages: English (default), Spanish, Japanese, Korean, Simplified Chinese, and Traditional Chinese.

The architecture leverages Next.js App Router conventions with a centralized translation library that provides type-safe access to localized content across all components.

Architecture

graph TD
    A[User Interface] --> B[Navbar Component]
    B --> C{Language Selector}
    C --> D[Set Language State]
    D --> E[i18n Context]
    E --> F[useTranslations Hook]
    F --> G[Translation Files]
    
    H[Localized README Files] --> I[website/public]
    H --> J[GitHub Repository]
    
    K[Spanish] --> G
    L[Japanese] --> G
    M[Korean] --> G
    N[Simplified Chinese] --> G
    O[Traditional Chinese] --> G
    P[English] --> G

Supported Languages

The project maintains translations for the following languages:

Sources: README.es.md Sources: README.ja.md Sources: README.ko.md Sources: README.zh-CN.md Sources: README.zh-TW.md

Core Components

i18n Library

The translation system is implemented in website/src/lib/i18n.tsx. This module provides the foundational translation infrastructure used throughout the React application.

Key exports include:

• Translation context provider
• useTranslations() hook for component-level translations
• Language configuration constants

Sources: website/src/components/Navbar.tsx:3

Language Configuration

The LANGUAGES array defines all supported locales with their metadata:

// Configuration includes:
{
  code: string,      // Locale identifier (e.g., 'en', 'zh-CN')
  flag: string,      // Emoji flag for UI display
}

Sources: website/src/components/Navbar.tsx:1-50

Language Selector Component

Component Structure

The language selector is implemented in the Navbar component, providing users with an accessible dropdown to change their preferred language.

graph LR
    A[Globe Icon] --> B[Button Click]
    B --> C{langOpen State}
    C -->|true| D[Show Dropdown]
    C -->|false| E[Hide Dropdown]
    D --> F[Language Options]
    F --> G[Select Language]
    G --> H[setLang Function]
    H --> I[Update Context]
    I --> J[Close Dropdown]

Features

Sources: website/src/components/Navbar.tsx:1-60

Implementation Details

<div ref={langRef} className="relative">
  <button
    onClick={() => setLangOpen(!langOpen)}
    aria-label="Change language"
  >
    <Globe className="w-4 h-4" />
    <span>{currentLang?.flag}</span>
  </button>
  <AnimatePresence>
    {langOpen && (
      <motion.div className="absolute right-0 top-10 w-44 ...">
        {LANGUAGES.map((l) => (
          <button
            key={l.code}
            onClick={() => { setLang(l.code); setLangOpen(false); }}
          />
        ))}
      </motion.div>
    )}
  </AnimatePresence>
</div>

Sources: website/src/components/Navbar.tsx:15-40

Usage Patterns

Component-Level Translations

Components access translations via the useTranslations() hook:

import { useTranslations } from "@/lib/i18n";

export default function ExampleComponent() {
  const t = useTranslations();
  
  return (
    <h2>{t.how.title}</h2>
    <p>{t.how.subtitle}</p>
  );
}

Sources: website/src/components/sections/HowItWorks.tsx:3-8

Translation Namespace Access

Translations are organized into namespaces (e.g., how, quality, footer). Components access specific sections:

const stages = t.how.stages;  // Array of stage data
const quality = t.quality;     // Quality section content
const footer = t.footer;       // Footer translations

Sources: website/src/components/sections/WhatIsIt.tsx Sources: website/src/components/sections/QualityStandards.tsx Sources: website/src/components/Footer.tsx:1-30

Documentation Localization

README Translations

The repository maintains full README translations in all supported languages. Each localized README follows the naming convention README.{locale}.md.

Citation Format

The Footer component displays BibTeX citation format that adapts to the selected language context:

@misc{awesome-agent-skills,
  author = {Hailey Cheng (Cheng Hei Lam)},
  title = {Agent Skill Index},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/heilcheng/awesome-agent-skills}
}

Sources: website/src/components/Footer.tsx:20-30

Adding New Translations

Adding a New Language

1. Create a new README.{code}.md file in the repository root
2. Add the language configuration to the LANGUAGES array in i18n.tsx
3. Add translation keys to the translation files
4. Update the language selector dropdown styling if needed

Translation Key Structure

interface Translations {
  nav: {
    // Navigation translations
  };
  how: {
    title: string;
    subtitle: string;
    steps: Array<{
      step: string;
      title: string;
      desc: string;
    }>;
  };
  quality: {
    // Quality standards translations
  };
  footer: {
    // Footer text including citation label
  };
}

Best Practices

Type Safety

The i18n system uses TypeScript to ensure type-safe access to translation keys. Always use the useTranslations() hook rather than direct object access to maintain type checking.

Lazy Loading

Translation content is loaded on-demand through the Next.js App Router, ensuring minimal bundle impact when users view the site.

Accessibility

The language selector includes proper ARIA labels and keyboard navigation support for screen reader users.

External Resources

• Official specification: agentskills.io/specification
• Project repository: github.com/heilcheng/awesome-agent-skills
• Agent Skill website: agent-skill.co

Sources: website/src/components/WikiSidebar.tsx

Related Pages

Related topics: Next.js Website Structure, Component Architecture

Deployment Configuration

Overview

The awesome-agent-skills project is a Next.js application that serves as a comprehensive directory for AI agent skills. The deployment configuration encompasses the build system, runtime environment, and hosting setup required to deploy this web application to production. Sources: website/README.md:1

The project is designed to be deployed on Vercel, the platform created by the developers of Next.js, which provides optimized deployment workflows for Next.js applications. Sources: website/README.md:32-34

Project Architecture

The project follows a modern Next.js architecture using the App Router pattern. This architectural choice impacts how the application is built and deployed. Sources: website/README.md:13

graph TD
    A[Source Code] --> B[Next.js Build]
    B --> C[Static Assets]
    B --> D[Server Components]
    D --> E[Vercel Edge Network]
    C --> E
    E --> F[End Users]
    
    G[Environment Variables] --> B
    H[Dependencies] --> B

Build System Configuration

Package Manager Support

The project supports multiple package managers for development and dependency installation:

Sources: website/README.md:7-15

Development Server

The development server runs locally on http://localhost:3000 by default. This can be configured through environment variables or the Next.js configuration file. Sources: website/README.md:17

# Starting the development server
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Production Build

The project uses standard Next.js build commands. When deployed to Vercel, the platform automatically detects the Next.js framework and runs the appropriate build commands. Sources: website/README.md:32-34

Font Optimization

The project uses next/font for automatic font optimization. This feature optimizes fonts and removes layout shifts for improved performance. Sources: website/README.md:21

// Example usage from Next.js documentation
import { Geist } from "next/font/google"

// Configuration is handled automatically by next/font

The font optimization occurs at build time, embedding font files directly into the application for faster page loads and reduced external requests.

Theme and Styling Configuration

Dark/Light Theme Support

The application implements a CSS custom properties-based theming system that supports both dark and light modes. The theming is defined through CSS variables at the :root level. Sources: website/index.html:1-50

:root{
  --bg:#000;
  --bg1:rgba(255,255,255,0.03);
  --bg2:rgba(255,255,255,0.05);
  --bg3:rgba(255,255,255,0.08);
  --border:rgba(255,255,255,0.08);
  --border2:rgba(255,255,255,0.12);
  --text:rgba(255,255,255,0.95);
}

The theme configuration includes:

Sources: website/index.html:8-16

Component-Level Theming

Individual components implement theme-aware styling using Tailwind CSS utility classes with dark mode variants:

// Example from Footer.tsx
className="text-white dark:text-neutral-900"
className="bg-zinc-900 dark:bg-white"

Sources: website/src/components/Footer.tsx:10-12

Environment Configuration

Public Environment Variables

The project exposes certain environment variables for public use. These are prefixed with NEXT_PUBLIC_ and are accessible in both server and client code. Sources: website/README.md:17

Site Metadata

The deployment includes the following metadata configuration:

Sources: website/index.html:2-7

Deployment to Vercel

Automatic Detection

Vercel automatically detects Next.js projects during deployment. The platform performs the following steps:

1. Detects next in package.json dependencies
2. Identifies the framework as Next.js
3. Runs the appropriate build command
4. Optimizes the output for edge deployment

Sources: website/README.md:32-34

Deployment Links

The project includes configured deployment links for easy access:

Sources: website/src/components/Footer.tsx:7-13

Linting and Code Quality

ESLint Configuration

The project uses ESLint for code quality enforcement. The configuration file is located at website/eslint.config.mjs. Sources: 查询文件: website/eslint.config.mjs

ESLint runs during the development process and CI/CD pipelines to ensure code consistency and catch potential issues before deployment.

Code Style Guidelines

Components follow consistent coding patterns:

• TypeScript for type safety
• Tailwind CSS utility classes for styling
• Dark mode support via dark: variants
• Consistent naming conventions

Sources: website/src/components/sections/QualityStandards.tsx:1-20

Post-Processing Configuration

PostCSS Configuration

The project uses PostCSS for processing CSS. Configuration is defined in website/postcss.config.mjs to support:

• Tailwind CSS
• Autoprefixer for browser compatibility
• CSS modules (if used)

Sources: 查询文件: website/postcss.config.mjs

CI/CD Considerations

The repository structure supports continuous integration and deployment through GitHub. When changes are pushed to the repository:

1. Vercel automatically pulls the latest changes
2. Build process runs automatically
3. Preview deployments are created for pull requests
4. Production deployment occurs on merges to main

Sources: website/src/components/sections/Contributing.tsx:25-35

Development Workflow

graph LR
    A[Local Development] -->|npm run dev| B[Dev Server]
    B --> C[Code Changes]
    C --> D[Auto Hot Reload]
    D --> B
    
    E[Git Push] --> F[Vercel]
    F --> G[Build]
    G --> H[Preview Deployment]
    
    I[Merge to Main] --> F
    H --> J[Production Deployment]

Related Documentation

For additional deployment and development information, refer to:

• Next.js Documentation - Official Next.js features and API documentation
• Vercel Platform - Deployment platform documentation
• Tailwind CSS - Styling framework documentation

Summary

The deployment configuration for awesome-agent-skills is straightforward, leveraging Next.js's built-in deployment capabilities with Vercel. Key aspects include:

1. Framework: Next.js with App Router
2. Hosting: Vercel (automatic detection and deployment)
3. Styling: Tailwind CSS with dark/light theme support
4. Fonts: Automatic optimization via next/font
5. Package Managers: Supports npm, yarn, pnpm, and bun
6. Code Quality: ESLint integration for consistent code standards

The configuration is production-ready out of the box and requires minimal additional setup for deployment.

Doramagic Pitfall Log

Doramagic extracted 10 source-linked risk signals. Review them before installing or handing real data to the project.

1. Project risk: Project risk needs validation

• Severity: medium
• Finding: Project risk is backed by a source signal: Project risk needs validation. Treat it as a review item until the current version is checked.
• User impact: The project should not be treated as fully validated until this signal is reviewed.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: identity.distribution | github_repo:1124920990 | https://github.com/heilcheng/awesome-agent-skills | repo=awesome-agent-skills; install=skills

2. Configuration risk: Configuration risk needs validation

• Severity: medium
• Finding: Configuration risk is backed by a source signal: Configuration risk needs validation. Treat it as a review item until the current version is checked.
• User impact: Users may get misleading failures or incomplete behavior unless configuration is checked carefully.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: capability.host_targets | github_repo:1124920990 | https://github.com/heilcheng/awesome-agent-skills | host_targets=claude

3. Capability assumption: README/documentation is current enough for a first validation pass.

• Severity: medium
• Finding: README/documentation is current enough for a first validation pass.
• User impact: The project should not be treated as fully validated until this signal is reviewed.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: capability.assumptions | github_repo:1124920990 | https://github.com/heilcheng/awesome-agent-skills | README/documentation is current enough for a first validation pass.

4. Maintenance risk: Maintainer activity is unknown

• Severity: medium
• Finding: Maintenance risk is backed by a source signal: Maintainer activity is unknown. Treat it as a review item until the current version is checked.
• User impact: Users cannot judge support quality until recent activity, releases, and issue response are checked.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: evidence.maintainer_signals | github_repo:1124920990 | https://github.com/heilcheng/awesome-agent-skills | last_activity_observed missing

5. Security or permission risk: no_demo

• Severity: medium
• Finding: no_demo
• User impact: The project may affect permissions, credentials, data exposure, or host boundaries.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: downstream_validation.risk_items | github_repo:1124920990 | https://github.com/heilcheng/awesome-agent-skills | no_demo; severity=medium

6. Security or permission risk: no_demo

• Severity: medium
• Finding: no_demo
• User impact: The project may affect permissions, credentials, data exposure, or host boundaries.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: risks.scoring_risks | github_repo:1124920990 | https://github.com/heilcheng/awesome-agent-skills | no_demo; severity=medium

7. Security or permission risk: Add Rug Munch Intelligence - Crypto Security & Analytics Agent Skills (x402, Base + Solana)

• Severity: medium
• Finding: Security or permission risk is backed by a source signal: Add Rug Munch Intelligence - Crypto Security & Analytics Agent Skills (x402, Base + Solana). Treat it as a review item until the current version is checked.
• User impact: The project may affect permissions, credentials, data exposure, or host boundaries.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: Source-linked evidence: https://github.com/heilcheng/awesome-agent-skills/issues/218

8. Security or permission risk: ⚡ Pay-per-call web search, scraping & AI tools for your agent — VERITY (L402)

• Severity: medium
• Finding: Security or permission risk is backed by a source signal: ⚡ Pay-per-call web search, scraping & AI tools for your agent — VERITY (L402). Treat it as a review item until the current version is checked.
• User impact: The project may affect permissions, credentials, data exposure, or host boundaries.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: Source-linked evidence: https://github.com/heilcheng/awesome-agent-skills/issues/226

9. Maintenance risk: issue_or_pr_quality=unknown

• Severity: low
• Finding: issue_or_pr_quality=unknown。
• User impact: Users cannot judge support quality until recent activity, releases, and issue response are checked.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: evidence.maintainer_signals | github_repo:1124920990 | https://github.com/heilcheng/awesome-agent-skills | issue_or_pr_quality=unknown

10. Maintenance risk: release_recency=unknown

• Severity: low
• Finding: release_recency=unknown。
• User impact: Users cannot judge support quality until recent activity, releases, and issue response are checked.
• Recommended check: Open the linked source, confirm whether it still applies to the current version, and keep the first run isolated.
• Evidence: evidence.maintainer_signals | github_repo:1124920990 | https://github.com/heilcheng/awesome-agent-skills | release_recency=unknown

Community Discussion Evidence

Doramagic exposes project-level community discussion separately from official documentation. Review these links before using awesome-agent-skills with real data or production workflows.

• ⚡ Pay-per-call web search, scraping & AI tools for your agent — VERITY ( - github / github_issue
• Add Rug Munch Intelligence - Crypto Security & Analytics Agent Skills (x - github / github_issue
• Project risk needs validation - GitHub / issue