memory-leak-detection_skill

Overview

This skill detects and helps fix memory leaks using heap snapshots, runtime profiling, and common leak-detection patterns. It provides practical code patterns, monitoring middleware, and tooling guidance for Node.js and Python. Use it to prevent OOM crashes and reduce memory-driven performance regressions.

How this skill works

The skill guides you to collect runtime memory data (heap snapshots, tracemalloc snapshots, memory samples) and compare before/after states to find growth sources. It includes lightweight monitoring middleware that samples heap usage, alerts on sustained growth, and suggestions for fixes like removing listeners, clearing timers, or using WeakMap/WeakRef. It also outlines production monitoring and force-GC strategies when available.

When to use it

• Memory usage grows steadily over time or after specific flows
• Out-of-memory (OOM) errors or repeated container restarts
• Performance degradation correlated with memory increases
• Investigating high memory consumption in production
• Verifying memory behavior after deploying a change

Best practices

• Take and compare heap snapshots or tracemalloc snapshots before and after suspicious operations
• Remove event listeners, clear timers, and unbind callbacks during teardown
• Use WeakMap/WeakRef for caches that should not prevent GC
• Limit cache sizes with bounded eviction to avoid unbounded growth
• Monitor memory in production and alert on sustained heap growth

Example use cases

• Capture Node.js heap snapshots before and after a long-running job and compare in Chrome DevTools
• Add middleware that samples process.memoryUsage() and warns when heapUsed grows >50% over samples
• Use tracemalloc in Python to identify top allocation sites after a test scenario
• Replace a global accumulating array with a bounded cache or WeakMap to allow GC
• Deploy a memory monitor that triggers alerts and optional force GC when thresholds are exceeded