home / skills / harborgrid-justin / lexiflow-premium / memory-leak-analysis-spas

memory-leak-analysis-spas skill

/frontend/.github-skills/memory-leak-analysis-spas

This skill helps you detect, isolate, and fix memory leaks in long-running React SPAs by profiling heaps and validating cleanup.

npx playbooks add skill harborgrid-justin/lexiflow-premium --skill memory-leak-analysis-spas

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
845 B
---
name: memory-leak-analysis-spas
description: Detect, isolate, and fix memory leaks in long-running React Single Page Applications.
---

# Memory Leak Analysis in SPAs

## Summary
Detect, isolate, and fix memory leaks in long-running React Single Page Applications.

## Key Capabilities
- Profile heap snapshots to find detached DOM trees.
- Identify closure retention cycles in hooks and listeners.
- Verify cleanup execution in `useEffect` and unmount life-cycles.

## PhD-Level Challenges
- Automate regression testing for memory consumption.
- Analyze leaks caused by hydration of large data structures.
- Trace leaks across React Router navigation boundaries.

## Acceptance Criteria
- Provide heap comparison reports before/after fixes.
- Demonstrate stable memory usage over extended sessions.
- Document memory profiling methodology.

Overview

This skill detects, isolates, and helps fix memory leaks in long-running React Single Page Applications. It focuses on profiling heap snapshots, locating detached DOM trees and closure retention cycles, and validating cleanup logic in hooks and unmount lifecycles. The goal is stable memory usage across extended sessions with reproducible reports.

How this skill works

The skill captures and compares heap snapshots to highlight objects that grow or persist unexpectedly, flagging detached DOM nodes and retained closures from event listeners or stale references. It inspects React hook implementations and lifecycle cleanup paths to verify useEffect and unmount handlers run correctly. It can automate repeated navigations and interactions to produce regression-aware memory reports and trace leaks across routing boundaries.

When to use it

  • After noticing gradual memory growth during prolonged app use
  • When users report performance degradation or increasing tab memory over time
  • During regression testing to prevent reintroduced leaks
  • Before release of features that hydrate or stream large datasets
  • When adding global listeners, singletons, or cross-route caches

Best practices

  • Capture baseline and post-interaction heap snapshots and compare diffs
  • Instrument components with weak references or debug IDs to spot detached DOM trees
  • Ensure every effect that registers listeners or timers returns a cleanup function
  • Use profiling during simulated long sessions with realistic navigation patterns
  • Automate memory assertions in CI to catch regressions early

Example use cases

  • Find a detached DOM tree left by a modal that doesn’t remove event listeners
  • Detect a closure in a custom hook retaining large arrays after route changes
  • Validate that server-side hydration of large datasets doesn’t leak on client navigation
  • Automate a long-session test that asserts stable resident set size across 1,000 route transitions
  • Produce before/after heap comparison reports to prove a fix reduced retained memory

FAQ

How do I reproduce a leak reliably?

Simulate realistic user flows repeatedly, capture heap snapshots at consistent points, and compare snapshots to identify growing retention patterns.

What signals indicate a real memory leak vs GC timing?

Consistent growth across multiple snapshots and sessions that does not resolve after forcing garbage collection indicates a leak; transient spikes that drop after GC are likely non-leaks.