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.
Source facts
- Repository
- ruvnet/ruflo
- Last source activity
- July 17, 2026 at 03:06
- Detected SKILL.md language
- English
- Stars
- 72,687
- Forks
- 8,609
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Source instructions · Read-only preview- 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" })`
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