How does this skill prevent WSL2 crashes?

It monitors memory thresholds in real-time and triggers alerts or cleanups before the host system runs out of resources, which is a common cause of WSL2 failure.

Does this monitoring significantly slow down agent startup?

The skill is designed to be lightweight. It includes performance testing scripts to measure and minimize monitoring overhead during container cold starts.

Can I set custom memory limits for different agents?

Absolutely. The skill supports environment variables like MEMORY_LIMIT and MEMORY_ALERT_THRESHOLD for fine-grained control over each container type.

Do I need Redis to use this memory monitoring skill?

Yes, it utilizes Redis for centralized metric reporting and coordination across multiple agent containers, allowing for a unified view of system health.

Docker Memory Analytics & Monitoring

Name: Docker Memory Analytics & Monitoring
Author: masharratt

bymasharratt

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Analytics & Monitoring

Monitors and reports real-time memory usage for Dockerized Claude agents to prevent system crashes and optimize resource allocation.

About

This skill integrates advanced memory tracking into Docker deployment workflows, specifically designed to address stability issues like WSL2 crashes in multi-agent environments. By reporting metrics to Redis and enabling threshold-based alerts, it allows for automatic container cleanup and provides deep insights into agent resource consumption. It is an essential tool for developers running multiple AI agent instances who need to maintain system health and performance through proactive observability and automated resource management.

Key Features

Real-time memory tracking per individual agent container
12 GitHub stars
Redis-based metric reporting for multi-agent swarm coordination
Enhanced Docker health checks with memory-aware exit codes
Threshold-based alerts to trigger automatic container cleanup
Performance data collection for cold-start and runtime optimization

Use Cases

Optimizing resource limits for specialized backend and frontend agent containers
Preventing WSL2 and host system crashes during heavy AI agent orchestration
Monitoring long-running autonomous agents for memory leaks and resource bloat

About

Key Features

Real-time memory tracking per individual agent container
12 GitHub stars
Redis-based metric reporting for multi-agent swarm coordination
Enhanced Docker health checks with memory-aware exit codes
Threshold-based alerts to trigger automatic container cleanup
Performance data collection for cold-start and runtime optimization

Use Cases

Optimizing resource limits for specialized backend and frontend agent containers
Preventing WSL2 and host system crashes during heavy AI agent orchestration
Monitoring long-running autonomous agents for memory leaks and resource bloat