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/volomydyr/design-engineer-plugin/ui-design-to-code-qanpx skills add volomydyr/design-engineer-plugin --skill ui-design-to-code-qagit clone --depth 1 https://github.com/volomydyr/design-engineer-pluginWrote 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/volomydyr/design-engineer-plugin/ui-design-to-code-qa)<a href="https://agentmods.dev/skills/volomydyr/design-engineer-plugin/ui-design-to-code-qa"><img src="https://agentmods.dev/badge/skills/volomydyr/design-engineer-plugin/ui-design-to-code-qa.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 | $0.00051 | $0.01877 |
| Opus 5 | $0.00026 | $0.00938 |
| Sonnet 5 | $0.00010 | $0.00375 |
| Haiku 4.5 | $0.00005 | $0.00188 |
Grade A, and why
ui-design-to-code-qa 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.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual Review
Why This Matters
AI-generated UI frequently contains specific, predictable issues: title case where sentence case was intended, incorrect spacing, hardcoded values instead of design tokens, creative interpretations of designs, and redundant components that duplicate existing ones. Catching these early prevents accumulated visual debt.
The most common violations are hardcoded values (AI writes specific color values instead of reusing established tokens) and redundant components (AI creates new components from scratch instead of reusing existing ones every time new designs are shared).
Beyond these technical issues, AI-generated UI has recognizable aesthetic fingerprints: the same fonts, the same color palettes, the same card layouts. These patterns are cataloged in the design critique references – consult them when the implementation looks "correct" but feels generic.
Interaction Method
If AskUserQuestion is available, use it for all prompts below.
If not, present each question as a numbered list and wait for a reply before proceeding. For multiSelect questions, accept comma-separated numbers (e.g. 1, 3). Never skip or auto-answer without explicit user consent.
Step 1: Determine Review Method
question: "How do you want to review the implementation?"
header: "Review Method"
options:
- label: "Compare with Figma designs (via Figma plugin)"
description: "Side-by-side comparison using Figma plugin data"
- label: "Review screenshots"
description: "Analyze provided screenshots of the implementation"
- label: "Review live URL (via Playwright)"
description: "Navigate to a URL and take automated screenshots"
- label: "Review code only"
description: "Audit the codebase for design system violations without visual comparison"
- label: "Review HTML prototype"
description: "Audit an HTML prototype file for UX and visual quality"
When "Review HTML prototype" is selected, read the HTML file at the path the user provides (default: .design-engineer-plugin/prototype/prototype.html). Run the UX non-negotiables check (Step 3) and an adapted visual audit that focuses on spacing, typography, hierarchy, and interactive states rather than design system token compliance (since prototypes use inline tokens, not a formal design system).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 179 lines · 51 tokens per session scan A aaecab0fed05
ui-design-to-code-qa is a skill published in the GitHub repository volomydyr/design-engineer-plugin (19 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 1,877 once invoked, about $0.0003 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.
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