grey-haven-memory-profiling

Memory leak detection involves analyzing heap snapshots to identify objects that remain in memory unexpectedly. Common leak patterns include unclosed event listeners, retained closures, and persistent timers. This Skill automatically identifies these patterns and provides remediation steps.

Yes, memory profiling works across Python services to analyze heap allocation, garbage collection behavior, and retention paths. The Skill generates standardized reports identifying memory growth, high consumption, and out-of-memory scenarios with actionable fixes.

Event listeners that are never removed accumulate in memory, preventing garbage collection. This Skill detects unclosed EventEmitter listeners and database connection leaks, then provides specific steps to fix retention issues and optimize memory.

Heap analysis traces object references backward to identify what keeps objects in memory. This Skill examines allocation timelines and retention chains across Node.js and browser environments, surfacing the exact leak sources and optimization opportunities.

Yes, memory profiling applies to Node.js, Bun, browser environments, and Python services. The Skill handles environment-specific garbage collection patterns and produces consistent profiling reports across all platforms.

Memory optimization for large datasets requires identifying unnecessary object retention and cache bloat. This Skill analyzes allocation patterns during dataset processing and recommends strategies to reduce peak memory consumption and prevent out-of-memory events.