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/performance-review

Performance-focused code review for identifying bottlenecks and optimization opportunities

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mastra
27k30 skills14 commands
Install
$ npx -y skills add mastra-ai/mastra --skill performance-review --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/performance-review

Context preview

The summary Claude sees to decide when to auto-load this skill.

Performance-focused code review for identifying bottlenecks and optimization opportunities

SKILL.md

performance-review.SKILL.md
name: performance-review
description: Performance-focused code review for identifying bottlenecks and optimization opportunities
version: 1.0.0
metadata:
  tags:
    - code-review
    - performance

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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