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。
This rule enforces the Don't Repeat Yourself principle to avoid code duplication and improve maintainability.
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。
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Extracting on the second occurrence (Rule of Two) | Two instances may be coincidentally similar; wrong abstraction is worse than duplication | Wait for the third occurrence before extracting; use the Rule of Three |
| Coupling unrelated concepts through shared helpers | Changes to one domain break the other; creates unexpected dependencies | Only extract when the shared logic genuinely represents the same domain concept |
| Over-abstracting to eliminate all apparent duplication | Creates complex indirection that requires reading 3 files to understand 1 operation | Prefer 3 readable duplicate lines over 1 inscrutable abstraction |
| Same constant defined in multiple configuration files | Values diverge silently; one-off changes cause hard-to-trace bugs | Single config module or environment variable; import everywhere |
| DRY applied to test code (reducing fixture duplication) | Test setup that's too DRY becomes hard to read in isolation | Tests should be self-contained; some duplication in test setup is acceptable |
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。