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image-to-code

image-to-code

github.com/plugin87Design & UI3.1k737 installsUpdated 2d ago
Repository

About image-to-code

A design skill for turning visual references into a design system and reconstructed UI. It derives color, typography, spacing, and layout choices, then builds and verifies a token-driven implementation without copying protected imagery or brand assets.

Key features

  1. 01Analyze palette, typography, spacing, and layout
  2. 02Generate a three-tier DTCG token theme
  3. 03Rebuild UI with shared component primitives
  4. 04Check contrast in light and dark themes
  5. 05Render screenshots and run verification checks

Use cases

  1. 01Derive design tokens from a reference screenshot
  2. 02Rebuild a reference interface as component-based UI
  3. 03Compare a rendered result with its visual reference

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/plugin87/ux-ui-agent-skills --skill image-to-code

After installing, type/image-to-codein Claude Code to confirm it loaded.

Details

From the project README

Skill: Image to Code

Step 0: is the kit here? This skill reads files from the kit. Check once: ls ${CLAUDE_SKILL_DIR}/../../../tokens >/dev/null 2>&1 && echo KIT_OK || echo KIT_MISSING On KIT_MISSING only the skill folders were installed, which is what npx skills add does. Say so plainly, point the user at npx ux-ui-agent-skills init or the plugin install, and stop. Do not guess the contents of a file you could not open.

Reconstruct a design from a visual reference as a real design system, not a one-off copy. Match the system (color/type/spacing language), never lift copyrighted imagery or brand assets.

Steps

  1. Read the reference like a designer. Infer and write down:
    • Palette: 1 dominant surface family, text colors, 1 primary action + at most 1 accent (sample the hues; don't guess random hex).
    • Type: family feel (geometric/grotesk/serif), the scale jumps, display vs. body contrast, weights.
    • Spacing & density: base unit, section rhythm, card padding; airy vs. compact.
    • Radius & depth: radius language (sharp/soft/pill), shadow vs. hairline separation.
    • Layout archetype + sequence: bounded hero / asymmetric split / dense bento / editorial stack (${CLAUDE_SKILL_DIR}/../../../taste/design-taste.md → Variance Mandate).
  2. Anchor to a known system if it's close: browse ${CLAUDE_SKILL_DIR}/../../../taste/aesthetic-systems.md / python3 ${CLAUDE_SKILL_DIR}/../../../scripts/design_systems.py search <term> and adopt that recipe to stabilize decisions.
  3. Build the token theme from the inferred values → 3-tier DTCG (design-tokens skill); generate a single theme.css. Verify every color pair with ${CLAUDE_SKILL_DIR}/../../../scripts/contrast.py / ${CLAUDE_SKILL_DIR}/../../../scripts/validate_contrast.py (light + dark). A sampled brand color that fails AA gets adjusted; taste never overrides POUR.
  4. Rebuild layout + components token-driven via ${CLAUDE_SKILL_DIR}/../../../frameworks/adapter-protocol.md + ${CLAUDE_SKILL_DIR}/../../../components/*: one shared primitive layer, all 8 states, a11y wired, no emoji (lucide), single theme. Apply taste (design-taste.md) so it doesn't regress to generic.
  5. Verify against the reference: render and screenshot it, compare side-by-side to the reference; run node ${CLAUDE_SKILL_DIR}/../../../scripts/gate.mjs measure_render.mjs, lint_hardcodes.py, taste_audit.mjs, and npm run verify.

Verification (definition of done)

  • npm run verify is 100% (tokens resolve, contrast AA light+dark, no hardcodes/emoji, real-render WCAG).
  • The rebuilt UI uses ONE inferred token theme; no per-section palettes.
  • A screenshot of the result visibly matches the reference's design language.

Honest limit: this matches the design system, not a pixel-perfect copy. Do not reproduce the reference's photographs, logos, or copyrighted copy. Substitute your own or generic placeholders.

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