What problem does it solve? Operating Amazon MSK Provisioned clusters requires distinguishing between Standard and Express broker behaviors that training data routinely conflates, leading to wrong advice on metrics, storage, patching, and sizing. This Skill provides verified operational guidance for performance troubleshooting, consumer lag diagnosis, storage management, cluster sizing, client tuning, CloudWatch monitoring, and maintenance operations. ## Core Features & Use Cases - Performance and Lag Troubleshooting: Diagnose high CPU, latency, traffic shaping, and consumer lag using the correct CloudWatch metrics for each broker type, with critical guardrails like never rebooting brokers while UnderReplicatedPartitions is above zero. - Deterministic Cluster Sizing: Run the bundled msk_sizing.py script to compute broker counts, instance choices, bottlenecks, and monthly costs across all Standard M5/M7g and Express M7g sizes. - Monitoring and Maintenance: Create CloudWatch alarms and dashboards, configure producers and consumers for IAM, SCRAM, or mTLS auth, and handle patching, version upgrades, and rolling restarts safely. - Use Case: A user reports consumer lag growing on an MSK cluster. The Skill first identifies the broker type via describe-cluster-v2, then walks through broker-side versus client-side diagnosis using the correct metrics for that type. ## Quick Start Ask the assistant to diagnose why your MSK cluster has high CPU or growing consumer lag, providing the cluster ARN so it can determine the broker type first.
