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performance-review

Review code for performance bottlenecks across database queries, memory use, frontend rendering, network requests, and algorithms. Use a structured checklist to identify optimization opportunities and classify their severity according to expected load and scale.

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Performance-focused code review for identifying bottlenecks and optimization opportunities

mastra-ai/mastra

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适用场景

Reviewing database queries for avoidable overhead
Checking memory leaks and unbounded resource growth
Reviewing React rendering and long-list performance
Finding redundant API calls and costly computations

包含的文件（2）

SKILL.md1.9 KB
references/performance-checklist.md1.1 KB

SKILL.md（原文）

安装前可以查看交给 AI 代理的指令内容。

Performance Review

When reviewing code for performance issues, check each category below. Reference the detailed checklist in references/performance-checklist.md.

Database & Queries

N+1 query patterns (queries inside loops)
Missing database indexes for frequently queried fields
Unbounded queries without LIMIT/pagination
SELECT * instead of selecting only needed columns
Missing connection pooling

Memory & Resources

Memory leaks: event listeners not removed, intervals not cleared, growing caches without bounds
Large objects held in memory unnecessarily
Unbounded arrays or maps that grow with usage
Missing cleanup in component unmount/destroy lifecycle

Rendering (Frontend)

Unnecessary re-renders (missing React.memo, useMemo, useCallback where appropriate)
Large component trees re-rendering for small state changes
Missing virtualization for long lists
Synchronous heavy computation blocking the main thread
Large bundle sizes from unnecessary imports

API & Network

Missing caching for frequently accessed, rarely changing data
Sequential API calls that could be parallelized
Missing pagination for large data sets
Over-fetching data (requesting more than needed)
Missing request deduplication

Algorithmic Complexity

O(n²) or worse operations on potentially large datasets
Repeated computation that could be memoized
String concatenation in loops (use array join or template literals)
Unnecessary sorting or filtering passes

Severity Levels

🔴 CRITICAL: Will cause performance degradation under normal load
🟠 HIGH: Will cause issues at scale
🟡 MEDIUM: Optimization opportunity with measurable impact
🔵 LOW: Minor optimization suggestion

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中文简介正在准备中。当前显示英语简介。

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