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iot-anomalies

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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.

plugins/ruflo-iot-cognitum/skills/iot-anomalies/SKILL.md

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Network or broad shell
Last checked
Nov 9, 2026

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81,394
Repository last updated
Nov 10, 2026
License
MIT

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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" })`
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