performance-review
Performance-focused code review for identifying bottlenecks and optimization opportunities
Best use case
performance-review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Performance-focused code review for identifying bottlenecks and optimization opportunities
Teams using performance-review should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/performance-review/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How performance-review Compares
| Feature / Agent | performance-review | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Performance-focused code review for identifying bottlenecks and optimization opportunities
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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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