iot-anomalies

v2026.09.24

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.

GitHub

Install command

npx skhub add ruvnet/iot-anomalies

Markdown

SKILL.md

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" })

Install command

npx skhub add ruvnet/iot-anomalies

Discovery