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AI/MLruvnet

iot-anomalies

Detect and classify telemetry anomalies on Cognitum Seed devices

Stars
56,726
Source
ruvnet/claude-flow
Updated
2026-05-31
Slug
ruvnet--claude-flow--iot-anomalies
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/ruvnet/claude-flow/HEAD/plugins/ruflo-iot-cognitum/skills/iot-anomalies/SKILL.md -o .claude/skills/iot-anomalies.md

Drops the SKILL.md into .claude/skills/iot-anomalies.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

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" })