plugin87/ux-ui-agent-skills

image-to-code

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.

View source

How to use

  1. Copy the installation command and review the source package before installing it in your own agent environment.
  2. Check the source document for required tools, credentials and supported hosts.
  3. Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/plugin87/ux-ui-agent-skills --skill image-to-code

Before you start

  • Installation does not include a model subscription, API allowance or permission to access your files.
  • Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document

Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.

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).
  1. 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.
  2. 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.
  3. 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.
  4. Verify against the reference — render and screenshot it, compare side-by-side to the reference; run node ${CLAUDE_SKILL_DIR}/../../../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.
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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