A CLI tool that uses AI to generate design rules and documentation from your Figma files. It analyzes your Figma designs and automatically extracts design tokens, components, and layout information into a structured format.

• 🎨 Extract design tokens (colors, typography, spacing, effects)
• 🧩 Generate component documentation
• 📐 Analyze layout structures
• 🤖 AI-powered pseudo-code generation
• 🔄 Real-time progress indicators
• 📝 Markdown output format

Run directly with npx:

npx fig4ai <figma-url> [--model=claude|gpt4] [--no-ai]

After generating your .designrules file, you can use it with AI-powered IDEs to automatically generate code and configurations:

Simply mention the .designrules file in your prompts:

> Generate a Tailwind config based on @.designrules file

> Create a Vue login page using the design tokens from @.designrules

> Build a React component library following @.designrules specifications

The AI will analyze your .designrules file and generate code that matches your design system's:

• Color palette
• Typography scales
• Spacing system
• Component structures
• Layout patterns
• Shadow effects
• Border styles
• And more...

fig4ai follows a sophisticated process to transform your Figma designs into AI-ready context:

1. Data Extraction

   • Connects to Figma API and retrieves comprehensive file data
   • Processes complex nested JSON structure containing all design information

2. Design Token Parsing

   • Parses the JSON structure hierarchically: Canvas > Frame > Component / Instance
   • Extracts design tokens (colors, typography, spacing, effects)
   • Organizes components and their instances with style references
   • Maintains relationship between components and their variants

3. AI-Powered Transformation

   • For each Canvas, sends structured data to GPT-4o
   • Generates semantic pseudo-code with complete styling context
   • Preserves all design decisions, constraints, and relationships
   • Includes accessibility considerations and responsive behaviors

4. Structured Documentation

   • Stores all design tokens and pseudo-code representations in .designrules
   • Uses Markdown format for maximum compatibility
   • Maintains hierarchical structure of the design system
   • Preserves all style references and component relationships

5. AI Context Integration

   • .designrules file serves as a comprehensive design context
   • When mentioned in AI-powered IDEs (Cursor/Windsurf), the file is parsed
   • AI understands the complete design system and can generate accurate code
   • Enables context-aware code generation based on your design system

In essence, fig4ai transforms your Figma file into a structured AI context, making your design system programmatically accessible to AI tools.

npx fig4ai <figma-url> [--model=claude|gpt4] [--no-ai]

Or if you've set FIGMA_DESIGN_URL in your .env file:

npx fig4ai [--model=claude|gpt4] [--no-ai]

The tool supports two AI models for enhanced design analysis:

1. Claude (Default)

   • Uses Anthropic's Claude 3 Sonnet model
   • Set CLAUDE_API_KEY in your environment variables
   • Generally better at understanding design context
   • More detailed component analysis

2. GPT-4o

   • Uses OpenAI's GPT-4 model
   • Set OPENAI_API_KEY in your environment variables
   • Alternative option if you prefer OpenAI

You can also run without AI enhancement:

npx fig4ai <figma-url> --no-ai

This will output raw design data in a structured format without AI processing.

# Required
FIGMA_ACCESS_TOKEN=your_figma_token

# Optional - At least one needed for AI features
CLAUDE_API_KEY=your_claude_api_key
OPENAI_API_KEY=your_openai_api_key

# Optional
FIGMA_DESIGN_URL=your_default_figma_url

The tool generates a .designrules file containing:

• Design token documentation
• Component specifications
• Layout structures
• AI-generated pseudo-code
• Style references
• Accessibility considerations

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

If you encounter any issues or have questions, please:

1. Check the issues page
2. Create a new issue if your problem isn't already listed