Agent Orchestration
Skill
/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.
Claude Code
$ npx -y skills add ruvnet/ruflo --skill iot-anomalies --agent claude-code
Codex
$ npx skills add ruvnet/ruflo --skill iot-anomalies -a codex -y
Cursor
$ npx skills add ruvnet/ruflo --skill iot-anomalies -a cursor -y
Gemini CLI
$ npx skills add ruvnet/ruflo --skill iot-anomalies -a gemini-cli -y
GitHub Copilot
$ npx skills add ruvnet/ruflo --skill iot-anomalies -a github-copilot -y
OpenCode
$ npx skills add ruvnet/ruflo --skill iot-anomalies -a opencode -y
Windsurf
$ npx skills add ruvnet/ruflo --skill iot-anomalies -a windsurf -y
SKILL.md
iot-anomalies.SKILL.mdname: 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@1.0.0 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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- Which coding agents can use iot-anomalies?
- iot-anomalies installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.