SkillsAdd

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.

First seen Jun 8, 2026

Installation

$ npx skills add plugin87/ux-ui-agent-skills --skill image-to-code
View on skills.sh ↗ GitHub repository ↗

Summary

  • 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.

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Also in this package

Other skills from plugin87/ux-ui-agent-skills · top by installs.

npx skills add plugin87/ux-ui-agent-skills
design-review 273 a11y-audit 242 redesign 236 design-tokens 232 design-code 229 apply-aesthetic 226 ux-writing 225 brandkit 224 design-qa 218 design-component 217

Browse all from plugin87/ux-ui-agent-skills

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 902
License MIT License
Default branch main
Open issues 1
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,721 B
  • docs SUMMARY.md 356 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 375 installs

SKILL.md

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

  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 — full-bleed hero / asymmetric split / bento / editorial stack (taste/design-taste.md → Variance Mandate).

  1. 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.
  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 scripts/contrast.py / 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 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.
  4. Verify against the reference — render and screenshot it, compare side-by-side to the reference; run node scripts/measurerender.mjs, linthardcodes.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.