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Anomaly Detection
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Analyze Session Message Costs
cost-session
ruvnet/ruflo
202
This tool provides a granular, per-message cost breakdown within a single conversation session. It serves as a deep-dive utility following an anomaly detection, allowing operators to pinpoint exactly which messages incurred the highest costs. It reveals critical cost components like output tokens, cache writes, and model escalations, helping users understand the true resource consumption beyond simple token counts.
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Detect IoT Telemetry Anomalies
iot-anomalies
ruvnet/ruflo
367
This utility detects and classifies various anomalies (such as spikes, flatlines, drift, or pattern breaks) within telemetry data streamed from IoT devices. It utilizes Z-score anomaly detection to provide actionable insights. This is crucial for investigating unusual device metrics, ensuring system health before deploying major firmware updates (canary advancement), or triaging fleet-wide operational alerts.
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Observing System Metrics and Anomaly Detection
observe-metrics
ruvnet/ruflo
382
A comprehensive tool for aggregating and displaying system metrics (counters, gauges, histograms) from the observability namespace. It computes baselines and flags anomalies by detecting deviations exceeding standard deviations, providing a clear snapshot of system health, error rates, and performance degradation trends.
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Anomaly Detection for Trading Signals
trader-signal
ruvnet/ruflo
281
This tool generates sophisticated trading signals by leveraging an advanced anomaly detection engine. It scans financial assets for various market anomalies—such as spikes, drifts, and oscillations—and classifies them. It then integrates neural network prediction and searches historical pattern databases to output highly ranked, actionable trading signals, complete with confidence scores and target parameters.
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