# 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.

- Skill: `ruvnet/iot-anomalies` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ruvnet/iot-anomalies`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ruvnet/iot-anomalies/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ruvnet (https://skillmd.com/u/ruvnet)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/ruvnet/iot-anomalies

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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@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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**Source:** [`ruvnet/ruflo`](https://github.com/ruvnet/ruflo) → `plugins/ruflo-iot-cognitum/skills/iot-anomalies/SKILL.md`