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detect-metric-anomaly

Detect statistical anomalies in time-series metrics using z-score calculations and thresholds.

3|Updated Dec 2, 2025
Search Tags:#monitoring#alerting#anomaly detection#z-score#metric analysis#thresholding
One-click install
npx skills add https://github.com/X-McKay/kubani --skill detect-metric-anomaly
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: detect-metric-anomaly
Source: https://github.com/X-McKay/kubani/tree/main/kubani/skills/general/analytics/detect-metric-anomaly
Command: npx skills add https://github.com/X-McKay/kubani --skill detect-metric-anomaly

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

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.

Frequently Asked Questions about detect-metric-anomaly

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect metric anomalies in time-series data?▼

To detect metric anomalies, this Skill analyzes time-series data using z-score calculations to quantify how far current values deviate from historical baselines, identifying spikes, drops, and trends for proactive monitoring.

What is z-score analysis for monitoring metric spikes and drops?▼

Z-score analysis for monitoring measures how many standard deviations a current metric value is from its historical mean. This statistical deviation quantifies unusual spikes or drops in metrics like CPU utilization or error rates.

Do I need historical baseline statistics to use z-score thresholding?▼

Yes, z-score thresholding requires historical baseline statistics, specifically the mean and standard deviation of your metrics. Accurate anomaly detection depends on these predefined baselines and configured warning and critical thresholds.

Can I use this for proactive alerting on CPU utilization and error rates?▼

Yes, you can use this for proactive alerting on metrics like CPU utilization, memory usage, and error rates. It checks current values against predefined thresholds to flag service degradation for SRE teams.

What's the best way to analyze sustained metric trends over time?▼

The best way to analyze sustained metric trends is using statistical trend analysis to identify continuous increases or decreases in metric values over a recent period, augmenting standard z-score spike detection with sustained deviation checks.

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