Image to code

Skill plugin87/ux-ui-agent-skills/.claude/skills/image-to-code

Turn Claude into a Senior Design Architect — DTCG design tokens, 42 components, WCAG 2.2 accessibility, any-framework code, 138 design systems, and runnable skills.

Install

npx -y skills add plugin87/ux-ui-agent-skills --skill image-to-code

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

What its author says it does

Copied from the file, not written here

Turn a reference image, screenshot, or mockup into token-driven, accessible code — infer the design system from the reference (palette, type scale, spacing, radius, layout archetype), map it to the 3-tier tokens, rebuild it, then verify with the kit's gates. Use when the user provides a design/screenshot and wants matching UI code.

SKILL.md

2.6 KB, 562 tokens by cl100k_base, as published. Nobody here has run it

Skill: Image to Code

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

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 — full-bleed hero / asymmetric split / bento / editorial stack (taste/design-taste.md → Variance Mandate).
Anchor to a known system if it's close — browse taste/aesthetic-systems.md / python3 scripts/design_systems.py search <term> and adopt that recipe to stabilize decisions.
Build the token theme from the inferred values → 3-tier DTCG (design-tokens skill); generate a single theme.css. Verify every color pair with scripts/contrast.py / scripts/validate_contrast.py (light + dark) — a sampled brand color that fails AA gets adjusted; taste never overrides POUR.
Rebuild layout + components token-driven via frameworks/adapter-protocol.md + 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.
Verify against the reference — render and screenshot it, compare side-by-side to the reference; run node scripts/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.