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 commands/nicohodt/claude-code-ui-ux-skill/design-reviewgit clone --depth 1 https://github.com/nicohodt/claude-code-ui-ux-skillWhat 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 | $0.00021 | $0.00174 |
| Opus 5 | $0.00010 | $0.00087 |
| Sonnet 5 | $0.00004 | $0.00035 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
design-review 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 yesterday.
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
100% identical to design-review — 0 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.
What it actually says
description: Run the 7-phase design review (WCAG AA, responsive, interaction) on a page or URL argument-hint: [url-or-file] [optional focus, e.g. "mobile nav" or "checkout flow"]
Use the design-review subagent to audit the target below. Drive a real browser via the Playwright MCP, screenshot each viewport tier, and return ranked findings (Blockers → Nitpicks).
Target: $1 Focus (optional): $2
If no target was given, ask for the running dev-server URL (or a file path). If a browser
cannot be opened, fall back to node scripts/design-audit.mjs and report only the heuristic
findings, clearly labeled as such. Fix Blockers and High-severity findings before reporting the
work as complete.
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.
- yesterday First seen · 16 lines · 0 tokens per session scan A df81f0c262ff
design-review is a command published in the GitHub repository nicohodt/claude-code-ui-ux-skill (2 stars, last pushed 29d ago), licensed MIT. It adds 21 tokens to every session and 174 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to design-review, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
dial
Live value calibration - 2-4 lettered candidates injected into the running app; click through them against real data, nothing written to source.
setup
SoDam-Design-Kit 설정 마법사 — config.json 생성 + shadcn 컴포넌트 스캔으로 component-map 초기 시드.
form-designer
Use when a form is losing people. Too many fields, validation that interrupts typing, unclear required fields, a multi-step flow with no sense of progress, or inputs with no labels.
checkout-specialist
Use when a cart or checkout is losing people. Abandoned carts, long payment forms, forced account creation, shipping cost revealed too late, weak trust signals, confusing order confirmation.
marketing-asset
마케팅 소재 파이프라인 — 제목/부제(+명함은 추가 정보) → Satori+Sharp로 OG·포스터·배너·명함 이미지 생성 (Phase 3).
create-design-md
Discover the project's visual identity, prototype 3 HTML design variants for Boss to compare in a browser, iterate, then lock in DESIGN.md (Stitch-format, machine + human readable). The single source of truth AI coding agents read before generating UI.