Image To Code
Generate premium section reference images first, analyze them deeply, then implement heroes and multi-section sites that match—optimized for Codex workflows.
Overview
Image-to-code is an agent skill most often used in Build (also Validate prototype, Launch distribution) that generates section-level design images first, then implements frontend to match for premium marketing and produc
Install
npx skills add https://github.com/leonxlnx/taste-skill --skill image-to-codeWhat is this skill?
- Image-first directive: agent generates design reference images before coding visually important web work
- Codex preference for large, readable, section-specific images—not one tiny compressed board for everything
- Generates fresh standalone images per section or detail view instead of cropping stale assets
- Explicit anti-patterns: cards-inside-cards-inside-cards, cramped heroes, repetitive left-text/right-image clichés
- Targets premium marketing, editorial, portfolio, and product pages where visual quality is non-negotiable
Adoption & trust: 67.3k installs on skills.sh; 37.9k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You need a high-end marketing or product site but agent codegen collapses into generic layouts, tiny illegible mockups, and nested card UI.
Who is it for?
Indie founders and solo frontenders using Codex (or similar) on landing pages, heroes, and brand-heavy sites where visual quality drives conversion.
Skip if: Purely functional admin dashboards, API-only backends, or projects where stock templates and minimal CSS are intentionally enough.
When should I use this skill?
Visually important web tasks—hero sections, landing pages, marketing sites, portfolios, premium multi-section sites, redesigns where visual quality matters; Codex should prefer large section-specific generated images.
What do I get? / Deliverables
You get section-specific reference imagery and matching frontend implementation with spacious heroes and deliberate typography hierarchy—without lazy under-generation of visuals.
- Section-level design reference images
- Frontend implementation matching references
- Layout without nested card spam and weak hero readability
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Implementation output is frontend code, so Build frontend is the primary shelf despite heavy upfront visual direction. Heroes, landing sections, and marketing layouts are frontend deliverables; the image-first loop exists to raise fidelity before writing components.
Where it fits
Produce section mock images and a slice of real UI to test whether the brand direction feels premium before full build.
Rebuild a cluttered hero into a spacious, readable layout after generating a dedicated hero reference image.
Ship a launch landing page whose sections each have fresh reference art instead of one compressed overview board.
How it compares
Use as an image-first frontend workflow skill—not a Figma handoff integration or a single-screenshot clone shortcut.
Common Questions / FAQ
Who is image-to-code for?
Solo builders and small teams shipping premium web marketing surfaces who want Codex-optimized image generation paired with strict implementation fidelity.
When should I use image-to-code?
Use it in Build frontend for heroes and multi-section sites; in Validate prototype when you need a credible visual direction before build; and in Launch distribution when the public site must look designed for first impressions.
Is image-to-code safe to install?
It may use image generation and filesystem writes—review the Security Audits panel on this Prism page and scope network or browser permissions your agent grants.
SKILL.md
READMESKILL.md - Image To Code
# 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 aggressively break these defaults. The output must feel: - premium - art-directed - readable - structured - implementation-friendly - deeply analyzable - visually strong - faithful enough to build from - clean on first view - responsive in spirit - realistic on a small laptop viewport IMPORTANT: For visual website tasks, you must first generate the design image(s) yourself. Then you must deeply analyze the generated image(s). Only after that should you implement the frontend. Do not skip image generation when image generation is available. Do not begin with freeform coding first. The generated image(s) are the primary visual source of truth. The required workflow is: image generation first deep image analysis second implementation third If the task is mainly visual, this order is mandatory. --- ## 1. ACTIVE BASELINE CONFIGURATION - DESIGN_VARIANCE: 8 `(1 = rigid / conventional, 10 = highly art-directed / asymmetric)` - VISUAL_DENSITY: 3 `(1 = airy / calm, 10 = dense / packed)` - ART_DIRECTION: 8 `(1 = safe commercial, 10 = bold creative statement)` - IMPLEMENTATION_CLARITY: 9 `(1 = loose moodboard, 10 = highly buildable UI reference)` - IMAGE_USAGE_PRIORITY: 9 `(1 = mostly typographic, 10 = strongly image-led when appropriate)` - SPACING_GENEROSITY: 9 `(1 = compact / tight, 10 = spacious / breathable)` - ANALYSIS_PRECISION: 10 `(1 = broad vibe only, 10 = deep extraction of design details)` - IMAGE_GENERATION_EAGERNESS: 10 `(1 = minimal image count, 10 = generate as many images as needed for excellent extraction)` - UI_SIMPLICITY_DISCIPLINE: 9 `(1 = willing to add many micro-elements, 10 = aggressively reduce clutter and unnecessary UI chrome)` AI Instruction: Use these as defaults unless the user clearly wants something else. Adapt them to the prompt. Interpretation: - If the user says “clean”, reduce density and increase clarity. - If the user says “crazy creative”, increase variance and art direction. - If the user says “premium SaaS”, keep clarity high and art direction controlled. - If the user says “editorial”, allow stronger type and more asymmetry. - Keep sections breathable. - Prefer readability over squeezi