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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionimage-to-codeExecute the skills CLI command in your project's root directory to begin installation:
Fetches image-to-code from Leonxlnx/taste-skill and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate image-to-code. Access via /image-to-code in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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| name | image-to-code |
| description | 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. |
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:
Standard AI output tends to collapse into repetitive defaults:
Your goal is to aggressively break these defaults.
The output must feel:
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 = rigid / conventional, 10 = highly art-directed / asymmetric)(1 = airy / calm, 10 = dense / packed)(1 = safe commercial, 10 = bold creative statement)(1 = loose moodboard, 10 = highly buildable UI reference)(1 = mostly typographic, 10 = strongly image-led when appropriate)(1 = compact / tight, 10 = spacious / breathable)(1 = broad vibe only, 10 = deep extraction of design details)(1 = minimal image count, 10 = generate as many images as needed for excellent extraction)(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:
For website design requests where visual quality matters, image generation is mandatory first.
This means:
Do not:
The image is the design source. The code is the translation layer.
Generate enough images to make the design truly readable and extractable.
Do not be lazy with image count.
If more images would improve:
then generate more images.
Strong rule:
Never reduce image count just for convenience if that harms quality.
Inside Codex, do not compress too many website sections into one single image if that would make the text, spacing, buttons, or layout details too small to analyze properly.
In Codex, prefer separate large images per section.
Default rule inside Codex:
This is preferred because:
Do not default to:
If necessary, generate more images rather than shrinking everything.
Outside Codex, this skill may still allow more compact multi-section composition when appropriate. Inside Codex, prioritize section clarity and extraction accuracy.
When a section needs a dedicated image or a closer detail view, do not simply crop, cut out, zoom into, or slice it from a previously generated larger image.
Do not:
Instead:
Reason: cropped images often destroy:
Fresh section-specific generation is strongly preferred over cropping.
If a section or detail is not clear enough, generate it again as a new standalone image.
This standalone regeneration should:
But it should also:
This is not a different design. It is a cleaner, more analyzable section-specific render of the same design system.
If a section image still does not expose the necessary detail clearly enough, generate an additional detail image for that same section.
Examples of useful secondary images:
These additional images exist to improve analysis and extraction quality.
Use them when needed for:
Do not hesitate to create a second or third extraction-oriented image for a section if the first image is too broad.
Analyze cleanly and systematically.
Do not do vague vibe-only analysis. Do not jump too fast from image to code.
For every generated section image, inspect cleanly:
If something is unclear, generate another image before coding.
The analysis should feel:
Before implementing anything, deeply analyze the generated image(s).
Do not just glance at them. Treat them like a design specification.
Carefully inspect and extract:
Your goal is to understand exactly why the generated website looks strong.
Only after this deep analysis should you implement the frontend.
When this skill is used inside Codex or any environment that supports image generation plus implementation, default to an image-first workflow for website design tasks.
Preferred execution order:
For visually important frontend tasks, do not begin by freely designing in code. Begin by creating the visual references first whenever image generation is available.
The images are the primary art-direction source. The code is the implementation layer.
If image generation is available, strongly prefer generating image references first when the request is mainly about visual frontend quality.
Trigger image-first workflow when the user asks for:
Direct-code first is more acceptable only when:
To avoid repetitive AI-looking output, internally choose a strong combination and commit to it consistently.
Do not mash everything into chaos. Pick a coherent visual direction and execute it clearly.
Choose 1:
Choose 1:
Choose 1:
Choose 1:
Choose 1:
Choose exactly 4 unique components:
Choose exactly 2:
These are not coding instructions. They are visual-direction cues the design should imply.
Every generated website section image must clearly communicate:
A developer or coding model should be able to look at the image(s) and understand how to build the website.
Do not produce vague abstract artwork when the request is for frontend. Default to real section comps.
The hero must feel cinematic, clear, and intentional.
The hero should feel calm, premium, and immediately readable.
Do:
Do not:
Strong preference:
Avoid:
The first visible website screen must feel usable and clean on a small laptop.
This means:
The hero and immediate first-view area should:
A smaller laptop should still see:
Do not default to box-in-box-in-box layouts.
Avoid:
Use boxes only when they have a clear purpose.
Prefer:
A section should not feel like a prison of containers. It should feel designed, open, and intentional.
Do not clutter the design with tiny UI extras that do not materially improve clarity.
Avoid:
Examples of things to avoid unless they are truly necessary:
Prefer:
Inside Codex, treat each section as its own analyzable unit.
If the user asks for:
General preference:
This section-first generation rule exists to prevent:
When generating a website design, think not only about the overall site but also about the internal image system used inside the website itself.
This may include:
If the site benefits from multiple images, include multiple image moments across the website.
Rules:
Images inside the website should usually sit inside clear, controlled, implementation-friendly frames.
Prefer:
Examples:
Avoid:
The goal is:
When text is readable in the generated section image, extract it and use it.
Especially inspect and extract:
If the text is too small to extract reliably:
Do not ignore text extraction. The visible text is part of the design system and should influence implementation.
Do not only notice that typography “looks nice”. Analyze it properly.
Extract and observe:
Use these findings during implementation. Do not flatten typography into a generic coded hierarchy.
Analyze spacing deliberately.
Inspect:
The goal is not exact pixel OCR. The goal is faithful spacing logic.
Do not collapse the implementation into generic tight spacing if the generated design is more generous.
Buttons and comp
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Prerequisites
Time Estimate
15-30 minutes to install and see first useful output
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use coding skills for boilerplate generation, code reviews, refactoring legacy code, writing tests, learning new frameworks, and debugging non-critical issues. Best for repetitive tasks where errors are easy to catch.
✗ Avoid when
Avoid for production security features (auth, encryption, payment processing), complex business logic requiring deep domain knowledge, performance-critical algorithms, or when learning fundamentals is more valuable than speed.
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Registry listing for image-to-code matched our evaluation — installs cleanly and behaves as described in the markdown.
image-to-code reduced setup friction for our internal harness; good balance of opinion and flexibility.
image-to-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
image-to-code has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added image-to-code from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
image-to-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
image-to-code reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: image-to-code is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: image-to-code is focused, and the summary matches what you get after install.
image-to-code is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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