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Ai Component Metadata

Community
cris-achiardi
ai-component-metadata

Generate AI-ready metadata for design system components to enable intelligent UI generation. Analyzes component structure and generates structured metadata that helps AI understand when and how to use components correctly. Useful for teams building AI-consumable design systems.

Overview

Publishercris-achiardi
Repositoryclaude-skills
Skill nameai-component-metadata
Stars
72
Forks
8
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cris-achiardi on GitHub. Read the source before you install it.

Installation

Install the Ai Component Metadata AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cris-achiardi/claude-skills/tree/main/skills/ai-component-metadata
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cris-achiardi/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/skills/ai-component-metadata .claude/skills/ai-component-metadata
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Component Metadata in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Component Metadata on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Component Metadata is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AI Component Metadata Generator

Generate structured, AI-consumable metadata for design system components to enable intelligent UI generation and component usage.

Quick Start

When analyzing a component, use the metadata schema template in scripts/generate_metadata.py or follow the manual process below:

bash
# Automatic generation (reads component file)
python scripts/generate_metadata.py path/to/Component.tsx

# Or use the template directly
cp assets/metadata-template.tsx your-component-metadata.tsx

Core Workflow

1. Analyze Component Structure

Identify:

  • Component composition (slots, children)
  • Available variants and states
  • Props and their types
  • Accessibility attributes

2. Generate Metadata

Create metadata following this schema:

javascript
export const componentMetadata = {
  component: {
    name: "ComponentName",
    category: "atoms|molecules|organisms",
    description: "Brief description",
    type: "interactive|display|container|input|navigation"
  },
  
  usage: {
    useCases: ["primary-use", "secondary-use"],
    requiredProps: [],
    commonPatterns: [
      {
        name: "pattern-name",
        description: "When to use",
        composition: "JSX example"
      }
    ],
    antiPatterns: [
      {
        scenario: "what-not-to-do",
        reason: "why",
        alternative: "what-instead"
      }
    ]
  },
  
  composition: {
    slots: {},
    nestedComponents: [],
    commonPartners: [],
    parentConstraints: []
  },
  
  behavior: {
    states: [],
    interactions: {},
    responsive: {}
  },
  
  accessibility: {
    role: "ARIA role",
    keyboardSupport: "description",
    screenReader: "behavior",
    focusManagement: "strategy",
    wcag: "AA"
  },
  
  aiHints: {
    priority: "high|medium|low",
    keywords: [],
    context: "when to use"
  }
}

3. Validate Metadata

  • Test with AI generation tasks
  • Verify in Storybook
  • Ensure examples are runnable

Component Categories

  • atoms: Basic building blocks (Button, Text, Input)
  • molecules: Simple combinations (Card, Chip, FormField)
  • organisms: Complex components (Header, Table, Form)

Advanced Features

For complex scenarios, see:

Working with Figma

When combining with Figma MCP:

javascript
// Figma provides visual context
const figmaContext = await Figma.get_design_context();

// Your metadata provides behavioral context
const componentMetadata = components.Button.metadata;

// AI combines both for complete understanding

Best Practices

  1. Keep examples real - Use actual, runnable code
  2. Focus on patterns - Document common usage patterns
  3. Include anti-patterns - Help AI avoid mistakes
  4. Validate through usage - Test with actual AI generation

Success Metrics

Your metadata is effective when:

  • AI uses existing components instead of recreating
  • Correct variants are selected based on context
  • Accessibility is maintained in generated code
  • Patterns are consistent across AI outputs

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ai Component Metadata AI skill do?

Generate AI-ready metadata for design system components to enable intelligent UI generation. Analyzes component structure and generates structured metadata that helps AI understand when and how to use components correctly. Useful for teams building AI-consumable design systems.

Why use Ai Component Metadata on TypingMind?

Because you install it once and use it with any model. Ai Component Metadata is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Component Metadata in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cris-achiardi/claude-skills/tree/main/skills/ai-component-metadata. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ai Component Metadata?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Component Metadata?

As many as you like. As long as a model supports skills, you can use Ai Component Metadata with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Component Metadata AI skill free?

Yes. It is published on GitHub by cris-achiardi under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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