Diagnose Kafka failures by mapping symptoms to Netdata operator playbook archetypes.
npx skills add https://github.com/netdata/skills --skill troubleshoot-kafka
Please help me install this Agent Skill. Skill: troubleshoot-kafka Source: https://github.com/netdata/skills/tree/main/skills/troubleshoot-kafka Command: npx skills add https://github.com/netdata/skills --skill troubleshoot-kafka
Diagnose Kafka incidents like ISR shrinkage, leaderless partitions, consumer lag, controller overload, and broker disk exhaustion with a structured, signal-driven approach instead of guessing from generic health checks.
Ask the AI: "Investigate my Kafka cluster and tell me whether the incident is caused by ISR shrinkage, leaderless partitions, consumer lag, controller overload, or broker disk exhaustion, then recommend the most likely remediation using Netdata MCP signals."
High-intent search queries and answers about installing and using this skill.
Troubleshoot Kafka ISR shrinkage and consumer lag by mapping reported symptoms to Netdata operator playbook failure archetypes. This approach uses MCP-based anomaly ranking and signal discovery over defined windows to guide step-by-step incident triage instead of generic health checks.
Diagnosing leaderless partitions and controller overload requires mapping the symptoms to known failure archetypes via a structured diagnostic tree. The Skill queries Netdata MCP signals to confirm the specific degradation occurring, narrowing down the root cause for on-call triage.
Verify Kafka remediation by re-querying the same Netdata MCP signals used during diagnosis. This post-remediation verification loop checks whether the anomalous telemetry signals for issues like broker disk exhaustion have returned to their expected ranges.
Yes, you can use this for on-call incident response without manual metric analysis. It applies rule-file guided diagnostics to automatically rank anomalies and map them to specific Kafka failure patterns like ISR shrinkage or leader election failures using Netdata telemetry.
Yes, this troubleshooting method works for broker disk exhaustion and related performance degradations. It queries Netdata via MCP to discover health signals and applies operator playbook logic to triage availability and performance issues across the Kafka cluster.
Generic Kafka health checks often miss root causes because they lack signal-driven triage. By applying an operator playbook diagnostic tree to MCP-based anomaly ranking over defined windows, you can pinpoint specific failure archetypes like leader election failures instead of guessing.
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