performance-analyzer_skill

Overview

This skill analyzes code performance to detect bottlenecks and provide concrete optimization suggestions for algorithms, queries, and resource usage. It focuses on execution time, memory usage, algorithmic complexity, and common anti-patterns to produce actionable reports. Use it to prioritize fixes and estimate performance gains before and after changes.

How this skill works

The analyzer inspects code paths, query patterns, and resource allocations to identify hotspots and classify impact (Critical, High, Medium). It detects algorithmic complexity issues (Big O), N+1 query patterns, memory leaks, caching opportunities, and inefficient async or rendering flows. Results are delivered as a prioritized report with code-level examples and specific remediation steps.

When to use it

• Investigating slow endpoints or pages with high latency
• Reducing CPU or memory usage in backend or frontend services
• Before deploying major features to estimate performance impact
• When profiling reveals hotspots but root cause is unclear
• Optimizing database access patterns and reducing query counts

Best practices

• Measure before and after: rely on profilers and real metrics, not guesses
• Target highest-impact hotspots first to maximize ROI
• Prefer algorithmic improvements (lower Big O) over micro-optimizations
• Balance performance with readability; document trade-offs
• Introduce automated monitoring (APM) to catch regressions early

Example use cases

• Detect and fix N+1 queries in an API by converting multiple queries into batched or joined queries
• Replace O(n²) routines with O(n) or O(n log n) implementations to handle large datasets
• Find and close memory leaks caused by unreleased event listeners or long-lived closures
• Recommend caching strategies (in-memory, Redis, CDN) for expensive computations or repeated queries
• Optimize frontend bundle size via tree shaking and code splitting; suggest virtualization for long lists