/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.
$ npx -y skills add ruvnet/ruflo --skill iot-anomalies --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/iot-anomalies
Context preview
The summary Claude sees to decide when to auto-load this skill.
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.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@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" })`
An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/ruflo
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