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.

Vedi sorgente

Come usare

  1. Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
  2. Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
  3. Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/plugin87/ux-ui-agent-skills --skill image-to-code

Prima di iniziare

  • L’installazione non include abbonamenti, quote API o accesso ai file.
  • Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale

Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.

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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