Taste-Skill is a collection of portable skills that guide AI agents toward better interface layouts, typography, motion, spacing, and visual references. It is used with coding agents such as Codex, Cursor, and Claude Code when building frontends. The catalogue entries are its own skills, instructions, and plugin for applying these design workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/leonxlnx/taste-skill/image-to-code-skillnpx skills add Leonxlnx/taste-skill --skill image-to-code-skillgit clone --depth 1 https://github.com/Leonxlnx/taste-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/leonxlnx/taste-skill/image-to-code-skill)<a href="https://agentmods.dev/skills/leonxlnx/taste-skill/image-to-code-skill"><img src="https://agentmods.dev/badge/skills/leonxlnx/taste-skill/image-to-code-skill.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00116 | $0.07704 |
| Opus 5 | $0.00058 | $0.03852 |
| Sonnet 5 | $0.00023 | $0.01541 |
| Haiku 4.5 | $0.00012 | $0.00770 |
Grade A, and why
image-to-code scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- image-taste-frontend — 100% identical, 2 lines differ
- image-to-code — 100% identical, 0 lines differ
- image-to-code — 100% identical, 0 lines differ
- image-to-code — 100% identical, 0 lines differ
- image-to-code — 100% identical, 23 lines differ
- image-to-code — 100% identical, 0 lines differ
- image-to-code — 100% identical, 0 lines differ
- image-to-code — 100% identical, 104 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 1,229 lines · 116 tokens per session scan A 4c060a8064a8
image-to-code is a skill published in the GitHub repository Leonxlnx/taste-skill (84,617 stars, last pushed 12d ago), licensed MIT. It adds 116 tokens to every session and 7,704 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
animated-sketch-diagram
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frontend-design
Guidance for distinctive, intentional visual design when building or reshaping UI, HTML/React artifacts, dashboards, charts, and visual reports. Use before implementation to choose a content-specific structure, typography, and visual system that does not read as a templated default.
web-artifacts-builder
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
frontend_design
Creates distinctive, production-grade frontend interfaces with high design quality, avoiding generic AI aesthetics.
edgeone skill scanner
Scan any agent skill for security risks before you install or use it. Powered by Tencent Zhuque Lab A.I.G (AI-Infra-Guard). 100% local static analysis — no file contents or credentials leave your device. Compatible with CodeBuddy, Cursor, Windsurf, Claude Code, OpenClaw and more. Triggers on: 这个 skill 安全吗, skill 安全扫描…
shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing…