# Memory Leak Debugger

> Identify and resolve unmanaged memory leaks, event listener leaks, and circular references across runtimes.

- Skill: `rmazrim/memory-leak-debugger` (Agent Skill)
- Install (CLI): `npx skillmds@latest add rmazrim/memory-leak-debugger`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rmazrim/memory-leak-debugger/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: RMAzrim (https://skillmd.com/u/rmazrim)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/rmazrim/memory-leak-debugger

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# Memory Leak Debugger

## Prerequisites & Dependencies
- Node.js with `--inspect` / Chrome DevTools, or Python `tracemalloc`, or Go `go tool pprof`
- Access to heap snapshots or memory profiling tools
- Basic understanding of GC roots and reference cycles

## Execution Steps
1. Start the application with memory profiling enabled (`node --inspect`, `python -m tracemalloc`, `go pprof http://localhost:6060`)
2. Exercise the app until the leak becomes noticeable (growing heap size, OOM warnings)
3. Take a heap snapshot and compare it over time (`heap-diff`, `tracemalloc.compare_to`, `pprof web`)
4. Identify retaining objects: look for large arrays, closures, DOM/event listeners, or unresolved Promises
5. Remove unexpected references: clear timers/intervals, remove event listeners (`removeEventListener`), use `WeakMap`/`weakref` where appropriate
6. Re-run the profile to confirm the leak is sealed, and add automated memory regression tests

```python
# Python example: detecting leaks with tracemalloc
import tracemalloc

tracemalloc.start()

def allocate_leak():
    # simulate unbounded growth
    data = [] * 10000  # grows each call
    return data

# Run and snapshot
snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.compare_to(tracemalloc.take_snapshot(), 'lineno')
for stat in top_stats[:5]:
    print(stat)
```