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Iot Anomalies
ASecurityDetect 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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- Added November 11, 2026
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Security analysis
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[](https://www.skillsdirectory.com/skills/lza6-iot-anomalies)---
name: iot-anomalies
description: 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.
allowed-tools: Bash(npx *) mcp__plugin_ruflo-core_ruflo__memory_store Read
argument-hint: "<device-id>"
---
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" })`
Attribution
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