What problem does it solve? Reverse-engineering the design system behind a visual reference is slow and error-prone: colors get eyeballed, tokens get invented, and the resulting brief is too vague for an AI builder or teammate to reconstruct faithfully. This Skill turns any visual source into a rigorous, confidence-marked design specification with real hex codes and machine-readable tokens. ## Core Features & Use Cases - Full design analysis: Analyzes images, website URLs, and Figma files through a 6-layer framework (identity, tokens, components, layout, reconstruction, brand rules) and outputs a design.md spec plus a W3C DTCG design-tokens.json. - Element mode: Copies a single element (navbar, card, 3D illustration) into a focused element.md with a rebuild prompt for code agents or a token-grounded image-generation prompt for tools like Midjourney or gpt-image. - Standalone CLI scripts: Seven Python tools for CSS variable extraction, multi-viewport Playwright capture, dominant color extraction, WCAG contrast checks, design.md linting, token drift audits, and Claude Design bundle export. - Use Case: Point it at vercel.com to extract 800+ CSS custom properties, generate a lint-validated design.md, then paste it into v0 to rebuild the page — or audit months later whether the live site has drifted from your captured tokens. ## Quick Start Ask the AI to extract the design system from a URL like https://vercel.com and generate a design.md with tokens and reconstruction notes.