Iot Anomalies

Detect and classify telemetry anomalies on Cognitum Seed devices. Use when investigating a device that's reporting odd metrics, before approving a firmware canary advancement, or when triaging fleet-wide health alerts.

thedixitjain 64bf7e1 878 B Updated 2 repo stars

File contents

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

Steps:

  1. npx -y -p @claude-flow/plugin-iot-cognitum@latest cognitum-iot anomalies DEVICE_ID
  2. Review detected anomaly types (spike, flatline, drift, oscillation, pattern-break, cluster-outlier)
  3. If score > 0.9, recommend quarantine
  4. Store anomaly pattern for learning: mcp__plugin_ruflo-core_ruflo__memory_store({ key: "iot-anomaly-DEVICEID", value: "TYPE at SCORE", namespace: "iot-anomalies" })

Source: ruvnet/rufloplugins/ruflo-iot-cognitum/skills/iot-anomalies/SKILL.md

thedixitjain/the-mega-skill-library/tree/main/library/general-purpose/iot-anomalies commit 64bf7e10bd

Frequently asked questions

npx skillmds add thedixitjain/iot-anomalies