accessibility
FreeEnsure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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This rule enforces the Don't Repeat Yourself principle to avoid code duplication and improve maintainability.
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See how to installReview the instructions the agent will receive before you install.
| 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.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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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.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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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
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