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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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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Performance-focused code review for identifying bottlenecks and optimization opportunities

mastra-ai/mastra
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  • 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

포함된 파일(2)

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

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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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