detect-metric-anomaly

What problem does it solve?

This Skill identifies unusual deviations in metric data, helping to proactively detect potential issues before they escalate.

Core Features & Use Cases

• Statistical Anomaly Detection: Uses z-score analysis to quantify how far a metric's current value deviates from its historical baseline.
• Threshold Monitoring: Checks metric values against predefined warning and critical thresholds.
• Trend Analysis: Identifies sustained increases or decreases in metric values over a recent period.
• Use Case: Automatically flag a web server's error rate if it spikes significantly above its normal operating range, alerting the SRE team to investigate potential service degradation.

Quick Start

Analyze the 'cpu_percent' metric for anomalies using its current value and historical baseline data.