Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Focuses on rules and best practices for mobile-first design and responsive typography using tailwind.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
width=device-width, initial-scale=1, mobile browsers render a zoomed-out desktop layout and all responsive CSS is ignored.| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Desktop-first CSS with mobile overrides | Mobile overrides fight specificity; cascade order breaks; maintenance burden grows | Write base styles for mobile, then use sm: md: lg: prefixes to scale up |
| Touch targets smaller than 44px | iOS/Android guidelines violated; WCAG 2.5.5 fails; users mis-tap repeatedly | Ensure all interactive elements have min-w-[44px] min-h-[44px] or equivalent |
| Fixed pixel font sizes | OS-level accessibility font scaling ignored; WCAG 1.4.4 violation | Use text-base (1rem) as mobile base; scale with responsive Tailwind text utilities |
| Missing viewport meta tag | Browser renders zoomed-out desktop layout; responsive CSS never activates | Always include <meta name="viewport" content="width=device-width, initial-scale=1"> |
| Unoptimized images served to mobile | Largest mobile performance bottleneck; LCP degrades; battery and data consumed | Serve WebP/AVIF with responsive srcset; lazy-load below-the-fold images |
Before starting:
cat .claude/context/memory/learnings.md
After completing: Record any new patterns or exceptions discovered.
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
日本語の概要は準備中です。原文の説明を表示しています。
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
日本語の概要は準備中です。原文の説明を表示しています。