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 skills add Kevin-Liu-01/Agent-Machines --skill taste-image-to-codegit clone --depth 1 https://github.com/Kevin-Liu-01/Agent-MachinesWrote 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/kevin-liu-01/agent-machines/taste-image-to-code)<a href="https://agentmods.dev/skills/kevin-liu-01/agent-machines/taste-image-to-code"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/taste-image-to-code/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kevin-liu-01/agent-machines/taste-image-to-code"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/taste-image-to-code.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00162 | $0.07831 |
| Opus 5 | $0.00081 | $0.03915 |
| Sonnet 5 | $0.00032 | $0.01566 |
| Haiku 4.5 | $0.00016 | $0.00783 |
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 9d 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.
This is a copy
98% identical to image-to-code — 57 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,236 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.
- 9d ago First seen · 1,236 lines · 162 tokens per session scan A b259a8039f3a
image-to-code is a skill published in the GitHub repository Kevin-Liu-01/Agent-Machines (29 stars, last pushed yesterday), licensed MIT. It adds 162 tokens to every session and 7,831 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to image-to-code, differing in 57 lines, and is treated as a copy.
Other skills, from other repositories
21st-ui
Find, install, and generate UI with 21st.dev. Use when the user asks for a UI component (pricing table, hero, navbar, dashboard, form, etc.), wants design inspiration, needs a brand logo as an SVG component, or wants to generate new UI from a prompt.
frontend-design-principles
Create polished, intentional frontend interfaces. Use this skill when building any UI — dashboards, admin panels, landing pages, marketing sites, or web applications. Routes to specialized guidance based on context.
theme
Apply brand and visual direction in a Mantle application using its repo-owned theme and UI contracts.
clean-interface-design
Design clean, calm, uncluttered interfaces that don't read as templated AI output. Use when building or restyling any UI (web, iOS, Android, macOS) and it needs visual hierarchy, restraint, spacing discipline, one clear primary action, and a cohesive look. Covers accent color, whitespace, corner radius, type scale…
design-taste-frontend
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
apple-platform-design
Design iOS, iPadOS, and macOS apps that feel native, in the Liquid Glass era. Use when building or reviewing Apple-platform UI — navigation, controls, sheets, materials, typography, haptics — or deciding what must change when porting from web/Android. Covers Apple's HIG, the Liquid Glass design language (iOS 26 /…