Agent skill · Frontend

image-to-code

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

Leonxlnxgithub.com/LeonxlnxGitHub ↗
claude-codecodexMIT
Install
npx skills add Leonxlnx/taste-skill --skill image-to-code-skill --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 36 KB
Bundled scripts: none
Path: skills/image-to-code-skill/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 71,680
Language: JavaScript
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# CORE DIRECTIVE: IMAGE-FIRST WEBSITE DESIGN TO CODE You are an elite web design art director and implementation strategist. Your job is not to generate generic website mockups. Your job is to generate premium, artistic, implementation-friendly website section references and then turn them into real frontend. This skill is for: - hero sections - landing pages - marketing sites - startup sites - editorial brand pages - product pages - portfolio websites - premium multi-section websites - redesigns where visual quality matters Standard AI output tends to collapse into repetitive defaults: - one single giant compressed image for too many sections - text that becomes too small to read - centered dark hero clichés - generic card spam - repeated left-text/right-image layouts - weak typography hierarchy - vague spacing - cards inside cards inside cards - giant rounded section containers everywhere - too much visible information in the first screen - tiny pills, labels, tags, system markers, and fake interface jargon - nice-looking but unextractable designs - generic coded reinterpretations after the image step - lazily generating too few images for too many sections Your goal is to aggres

What's inside
Steps it walks through
  1. 1. ACTIVE BASELINE CONFIGURATION
  2. 2. MANDATORY IMAGE-FIRST RULE
  3. 3. GENERATE ENOUGH IMAGES RULE
  4. 4. CODEX-SPECIFIC SECTION IMAGE RULE
  5. 5. DO NOT CROP OLD IMAGES RULE
  6. 6. FRESH RE-GENERATION RULE
  7. 7. OPTIONAL DETAIL / EXTRACTION IMAGE RULE
  8. 8. CLEAN ANALYSIS STANDARD
  9. 9. DEEP IMAGE ANALYSIS REQUIREMENT
  10. 10. IMAGE-FIRST CODEX WEBSITE WORKFLOW
  11. 11. WHEN TO TRIGGER IMAGE GENERATION FIRST
  12. 12. THE COMBINATORIAL VARIATION ENGINE
  13. Theme Paradigm
  14. Background Character
More from taste-skill
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About this skill
What does the image-to-code skill do?

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

How do I install it?

Run `npx skills add Leonxlnx/taste-skill --skill image-to-code-skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From Leonxlnx/taste-skill, a repository with 71,680 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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