Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Specifies the preferred syntax for asynchronous operations using async/await and onMount for component initialization. This results in cleaner and more readable asynchronous code.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
.then() chains — async/await produces linear, readable code; .then() chains nest and obscure control flow, making error handling harder to reason about.onMount (Svelte) or useEffect (React) for async component initialization — direct top-level async in component body can run before the DOM is ready; lifecycle hooks guarantee correct timing.forEach with async callbacks — array.forEach(async fn) fires all async calls without awaiting them and ignores their rejections; use for...of for sequential or Promise.all(array.map(async fn)) for parallel.try/catch with async/await or .catch() with .then() chains..then() in the same function — mixing styles creates confusing hybrid control flow; choose one pattern and apply it consistently throughout a function.| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
array.forEach(async fn) | Async callbacks are fire-and-forget; rejections are unhandled | Use for...of (sequential) or Promise.all(arr.map(...)) (parallel) |
| Unhandled promise rejections | Crashes Node.js; silently fails in browser | Always wrap in try/catch or add .catch() |
Mixing .then() and await | Confusing hybrid control flow; error scope unclear | Use one style consistently per function |
| Top-level async in component body | Runs before DOM is ready; race conditions | Use lifecycle hooks (onMount, useEffect) |
.then() chains 3+ levels deep | Callback pyramid; hard to debug | Convert to async/await for linear readability |
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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