grey-haven-memory-profiling

# Memory Profiling Skill

Identify memory leaks, inefficiencies, and optimization opportunities in running applications through systematic heap analysis and allocation profiling.

## Description

Specialized memory profiling skill for analyzing allocation patterns, heap usage, garbage collection behavior, and memory retention in JavaScript/TypeScript (Node.js, Bun, browsers) and Python applications. Detect memory leaks, optimize memory usage, and prevent out-of-memory errors.

## What's Included

### Examples (`examples/`)
- **Memory leak detection** - Finding and fixing common leak patterns
- **Heap snapshot analysis** - Interpreting Chrome DevTools heap snapshots
- **Allocation profiling** - Tracking memory allocation over time
- **Real-world scenarios** - E-commerce app leak, API server memory growth

### Reference Guides (`reference/`)
- **Profiling tools** - Chrome DevTools, Node.js inspector, Python memory_profiler
- **Memory concepts** - Heap, stack, GC algorithms, retention paths
- **Optimization techniques** - Object pooling, weak references, lazy loading
- **Common leak patterns** - Event listeners, closures, caching, timers

### Templates (`templates/`)
- **Profiling report template** - Standardized memory analysis reports
- **Heap snapshot comparison template** - Before/after analysis
- **Memory budget template** - Setting and tracking memory limits

### Checklists (`checklists/`)
- **Memory leak checklist** - Systematic leak detection process
- **Optimization checklist** - Memory optimization verification

## Use This Skill When

- ✅ Memory usage growing continuously over time
- ✅ High memory consumption detected (> 500MB for Node, > 1GB for Python)
- ✅ Performance degradation with prolonged runtime
- ✅ Out of memory errors in production
- ✅ Garbage collection causing performance issues
- ✅ Need to optimize memory footprint
- ✅ User mentions: "memory leak", "memory usage", "heap", "garbage collection", "OOM"

## Related Agents

- `memory-profiler` - Aut