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 mgiovani/cc-arsenal --skill review-designgit clone --depth 1 https://github.com/mgiovani/cc-arsenalWrote 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/mgiovani/cc-arsenal/review-design)<a href="https://agentmods.dev/skills/mgiovani/cc-arsenal/review-design"><img src="https://agentmods.dev/badge/skills/mgiovani/cc-arsenal/review-design/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/mgiovani/cc-arsenal/review-design"><img src="https://agentmods.dev/badge/skills/mgiovani/cc-arsenal/review-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00185 | $0.03214 |
| Opus 5.5 | $0.00074 | $0.01286 |
| Sonnet 5.5 | $0.00037 | $0.00643 |
| Haiku 4.5 | $0.00018 | $0.00321 |
Grade A, and why
review-design 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 22d 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Review
Comprehensive UX/UI/design quality analysis across 8 audit dimensions, each mapped to authoritative standards. This skill only analyzes: it identifies design issues, explains findings against a cited criterion, and suggests fix approaches without making code changes.
It supports two input modes:
- Live mode: audits a running URL via the
agent-browserCLI (screenshots + DOM/accessibility snapshot). - Static mode: audits a codebase (CSS/components/design tokens) via Grep.
When both a URL and a codebase target are supplied, it runs both and emits two separate reports.
Audit Taxonomy (8 Dimensions)
| # | Dimension | Key Standards |
|---|---|---|
| 1 | Visual Hierarchy & Layout | NN/g, Refactoring UI, Laws of UX, MD3 8pt grid |
| 2 | Typography | Butterick, Refactoring UI, MD3, Apple HIG |
| 3 | Color & Theming (incl. Dark Mode) | MD3 color roles, 60-30-10, WCAG 1.4.1/1.4.3 |
| 4 | Depth & Elevation (Shadows) | MD3 elevation, Refactoring UI, Josh Comeau |
| 5 | Components & Affordance (Buttons/Icons) | MD3 buttons/icons, Apple HIG, NN/g |
| 6 | Feedback & States | NN/g visibility-of-status, MD3 state layers |
| 7 | Motion & Microinteractions | NN/g animation, Laws of UX (Doherty), MD3 motion |
| 8 | Accessibility (cross-cutting) | WCAG 2.2 AA |
Measurable criteria for dimensions 1–4 are in references/criteria-foundations.md; dimensions 5–8 are in references/criteria-interaction.md.
Anti-hallucination guidelines
Design reviews must be based on actual evidence, never assumptions. This is critical:
- Observe before claiming: never report an issue without reading the code (static) or viewing the screenshot/snapshot (live)
- Evidence-based findings: every finding cites a file path + line number (static) OR a screenshot region + DOM ref (live)
- Cite a criterion: every finding maps to a criterion ID and an authoritative citation (WCAG SC, MD3 spec, etc.)
- Measure, don't estimate: report actual values (contrast ratio, px size, ms duration), not guesses. In static mode there is no rendered page to sample from, so compute the contrast ratio from the two hex/rgb values found in the CSS/tokens with the sibling
product-design-tokensskill'sscripts/contrast.py --pair FG BG(otherwise a python3 one-liner of the WCAG relative-luminance formula, given in references/agent-prompts.md); never eyeball a ratio - Applicable-only scoring: only score dimensions that apply to the target; never penalize what cannot be observed
- State what was NOT checked: every report ends with an explicit coverage gap section
- No invented standards: only reference real WCAG SCs, MD3 specs, and HIG guidance
What ships with it
10 files 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.
- evals/evals.json 3.8 KB
- evals/fixtures/clean-fixture.css 716 B
- evals/fixtures/clean-fixture.html 337 B
- evals/fixtures/low-contrast-fixture.css 619 B
- evals/fixtures/low-contrast-fixture.html 323 B
- evals/trigger-eval.json 2.1 KB
- references/agent-prompts.md 13 KB
- references/criteria-foundations.md 8.0 KB
- references/criteria-interaction.md 7.7 KB
- references/report-template.md 6.1 KB
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.
- 22d ago Changed · -1 lines ef71ca127def
- 1mo ago Changed · +8 lines ec2570754a5e
- 1mo ago Changed · -1 lines · -13 tokens per session 8d22d0cbcb59
- 1mo ago Changed · +1 lines 341e062f1df2
- 1mo ago First seen · 257 lines · 198 tokens per session scan A 92a2aa7e4a86
review-design is a skill published in the GitHub repository mgiovani/cc-arsenal (8 stars, last pushed 2d ago), licensed MIT. It adds 185 tokens to every session and 3,214 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-08-31.
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