Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Game development expert including DragonRuby, Unity, and game mechanics
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
When reviewing or writing code, apply these guidelines:
When reviewing or writing code, apply these guidelines:
When reviewing or writing code, apply these guidelines:
When reviewing or writing code, apply these guidelines:
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Instantiating/destroying objects every frame | GC pressure causes frame hitches; performance degrades with entity count | Use object pools; reuse pre-allocated instances by resetting and re-enabling |
| Game logic in the render step | Frame-rate-dependent behavior; desync between logic and display | Separate fixed-rate update loop from variable-rate render loop |
| Ad-hoc if/else for entity states | Impossible edge cases; transitions undefined for unexpected state combos | Use explicit state machine with defined transitions and entry/exit hooks |
| Direct input polling in entity update | Breaks remapping, replay, and multiplayer input sync | Route all input through centralized input manager updated once per frame |
| Optimizing without profiling | Wrong bottleneck targeted; CPU/GPU budget guesses are often wrong | Profile with Unity Profiler or platform tools; optimize measured hot paths only |
This expert skill consolidates 4 individual skills:
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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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.
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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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