/performance-review
Performance-focused code review for identifying bottlenecks and optimization opportunities
$ npx -y skills add mastra-ai/mastra --skill performance-review --agent claude-codeHow 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 →
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/performance-review
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Performance-focused code review for identifying bottlenecks and optimization opportunities
SKILL.md
performance-review.SKILL.mdname: performance-review
description: Performance-focused code review for identifying bottlenecks and optimization opportunities
version: 1.0.0
metadata:
tags:
- code-review
- performancePerformance 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
Read more
name: performance-review
description: Performance-focused code review for identifying bottlenecks and optimization opportunities
version: 1.0.0
metadata:
tags:
- code-review
- performancePerformance 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
Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack. It includes everything you need to go from early prototypes to production-ready applications.
Repo: mastra-ai/mastra
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