Atlas / Skills / leonxlnx / Image To Code Skill

Image To Code SkillSAFE

skills/leonxlnx/image-to-code-skill

Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
1 documented
License
MIT
Stars
92,635
01

Overview

Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop

Read from source at commit c83c84311a38OBSERVED · 2026-10-05
02

Host compatibility

What the documentation claims. We have not run a compatibility test.

HostStatusNotes
codexmentioned
03

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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.
---

# 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 squeezing too much into one image.
- In Codex, bias strongly toward larger, more analyzable section images.
- If more images would improve extraction quality, generate more images.
- Do not be lazy with image count.
- Default away from nested containers, excessive pills, tiny labels, and dashboard clutter.

---

## 2. MANDATORY IMAGE-FIRST RULE

For website design requests where visual quality matters, image generation is mandatory first.

This means:
1. generate the design image or image set yourself first
2. deeply inspect and analyze the generated image(s)
3. extract the design system from them
4. implement the frontend only after that

Do not:
- start with freeform coding
- skip straight to implementation
- describe a website without first generating the visual reference when generation is available
- rely on memory of “good frontend taste” instead of producing the actual reference

The image is the design source.
The code is the translation layer.

---

## 3. GENERATE ENOUGH IMAGES RULE

Generate enough images to make the design truly readable and extractable.

Do not be lazy with image count.

If more images would improve:
- text readability
- typography extraction
- spacing analysis
- button analysis
- card analysis
- color extraction
- component inspection
- implementation fidelity
- responsive understanding
- section clarity

then generate more images.

Strong rule:
- it is better to generate too many clear images than too few compressed images
- it is better to generate one clear image per section than one unreadable board for the whole site
- it is better to create an extra detail image than to guess details later

Never reduce image count just for convenience if that harms quality.

---

## 4. CODEX-SPECIFIC SECTION IMAGE RULE

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 r
04

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-05 · audit v0.4.1 · source sha c83c84311a38full audit observations/trust-audit/skill/leonxlnx__image-to-code-skill.json · Report an issue / request a re-scan
05

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-05c83c84311a38SAFEB89first audit
06

Questions

What does the Image To Code Skill skill do?

Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop

Is Image To Code Skill safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Image To Code Skill access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

Which assistants does Image To Code Skill work with?

Its documentation mentions codex. That is what the text claims, not a compatibility test we ran.

How current is this page?

The grade is for one exact copy of the source (c83c84311a38), read on 2026-10-05. The repository is watched, and a new audit runs when it changes — this is the first audit.

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