errors

To aggregate cross-service errors for root-cause analysis, the Skill collects data from multiple discovery sources to quantify error rates, identify top types, and correlate spikes with deployments and code changes.

Yes, you can correlate error spikes with deployments and code changes. The analysis detects patterns and matches error activity against recent deployments to identify potential root causes and guide remediation.

The best way to detect new versus recurring errors is using pattern detection across aggregated tracking sources. This surfaces actionable insights by identifying error spikes, cascades, and affected user clusters.

Yes, it works with existing tools by implementing capability-based discovery across error-tracking, APM, and logging platforms to aggregate data and generate a structured error analysis output.

You should not use this Skill for general observability or latency tracing tasks. It is specifically designed for error analysis, incident impact quantification, and deployment correlation rather than overall system monitoring.

To generate an investigation path for affected user clusters, initiate an error investigation across discovered sources. The Skill returns a structured report with root-cause hypotheses and recommended engineering actions.