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ruvnet / 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.

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

Steps:

  1. npx -y -p @claude-flow/plugin-iot-cognitum@1.0.0 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" })