Detect IoT Telemetry Anomalies

v20260716

iot-anomalies

This utility detects and classifies various anomalies (such as spikes, flatlines, drift, or pattern breaks) within telemetry data streamed from IoT devices. It utilizes Z-score anomaly detection to provide actionable insights. This is crucial for investigating unusual device metrics, ensuring system health before deploying major firmware updates (canary advancement), or triaging fleet-wide operational alerts.

IoT Telemetry Anomaly Detection Monitoring Diagnostics Data Analysis

Get Skill

367 downloads

Overview

Run Z-score anomaly detection on a device's recent telemetry.

Steps:

npx -y -p @claude-flow/plugin-iot-cognitum@latest cognitum-iot anomalies DEVICE_ID
Review detected anomaly types (spike, flatline, drift, oscillation, pattern-break, cluster-outlier)
If score > 0.9, recommend quarantine
Store anomaly pattern for learning:

mcp__plugin_ruflo-core_ruflo__memory_store({ key: "iot-anomaly-DEVICEID", value: "TYPE at SCORE", namespace: "iot-anomalies" })

Info

Category Data Science

Name iot-anomalies

Version v20260716

Size 837B

Source ruvnet/ruflo

Updated At 2026-07-18