Catalogskills.sh · ruvnet

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.

Review advisedVerified hub730 installs

Savant verdict: Review advised

Security analysis or source-hub review identified patterns requiring attention.

Live evaluation

Not evaluated yet. Workspaces can request a live evaluation.

Security assessment

Scan pending.

Structure

  • No license declaredConfirm you may reuse this skill before importing it into your repository.
  • The source hub's security checks flagged this skillGen Agent Trust Hub: warn (This skill performs IoT anomaly detection by downloading and executing a package from the NPM registry. It relies on a remote dependency with no version pinning, which introduces supply chain risks, and it processes external telemetry data without explicit sanitization or boundary markers.); Snyk: warn (Risk: MEDIUM · 1 issue)

SKILL.md

---
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" })`