Iot Anomalies
- 642 installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/ruflo
iot-anomalies is an agent skill that detects and classifies telemetry anomalies on Cognitum Seed devices for developers investigating odd metrics or fleet-wide health alerts.
About
iot-anomalies is a ruvnet/ruflo skill that runs Z-score anomaly detection on a Cognitum Seed device's recent telemetry via npx -y -p @claude-flow/plugin-iot-cognitum@latest cognitum-iot anomalies DEVICE_ID. It reviews six anomaly types—spike, flatline, drift, oscillation, pattern-break, and cluster-outlier—and recommends quarantine when the anomaly score exceeds 0.9. Developers invoke it when a device reports odd metrics, before approving firmware canary advancement, or while triaging fleet-wide health alerts. Detected patterns can be stored through mcp__claude-flow__memory_store for learning across incidents. Allowed tools include Bash(npx *), memory_store, and Read. Catalog metadata lists 440 installs. iot-anomalies fits Claude-flow IoT operations where statistical telemetry screening must precede canary promotion or fleet incident response instead of manual threshold guessing.
- iot-anomalies
Iot Anomalies by the numbers
- 642 all-time installs (skills.sh)
- +6 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #593 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 642 |
|---|---|
| repo stars | ★ 67k |
| Last updated | August 4, 2026 |
| Repository | ruvnet/ruflo ↗ |
How do you detect IoT telemetry anomalies?
Use iot-anomalies for development tasks
Who is it for?
Developers operating Cognitum Seed IoT fleets who need Z-score anomaly triage before firmware canary promotion or incident response.
Skip if: Developers without @claude-flow/plugin-iot-cognitum or teams monitoring non-IoT application metrics without device telemetry streams.
When should I use this skill?
User investigates odd device metrics, triages fleet health alerts, or evaluates firmware canary advancement on Cognitum Seed devices.
What you get
Anomaly classification report with six anomaly types, Z-score results, quarantine recommendation, and stored pattern in AgentDB memory.
- Anomaly classification report
- Quarantine recommendation
- Stored anomaly pattern
By the numbers
- Detects 6 telemetry anomaly types on Cognitum Seed devices
- Recommends quarantine when anomaly score exceeds 0.9
Files
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__claude-flow__memory_store({ key: "iot-anomaly-DEVICEID", value: "TYPE at SCORE", namespace: "iot-anomalies" })
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Pick iot-anomalies for statistical Z-score device telemetry screening instead of static threshold alerts without six-type anomaly classification.
FAQ
Which anomaly types does iot-anomalies detect?
iot-anomalies classifies six anomaly types on device telemetry: spike, flatline, drift, oscillation, pattern-break, and cluster-outlier. Detection uses Z-score analysis via the cognitum-iot anomalies CLI command.
When does iot-anomalies recommend quarantine?
iot-anomalies recommends quarantine when the anomaly score exceeds 0.9 on a Cognitum Seed device. Developers invoke it before firmware canary advancement or while triaging fleet-wide health alerts.
What CLI does iot-anomalies execute?
iot-anomalies runs npx -y -p @claude-flow/plugin-iot-cognitum@latest cognitum-iot anomalies with a device-id argument-hint. Results can be stored for learning via mcp__claude-flow__memory_store.