计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
anomaly-detection【效率与协作】Use Darts anomaly scorers, detectors, aggregators, and forecasting anomaly wrappers with correct score and label semantics.
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Use this sub-skill when the user needs Darts anomaly scores, binary anomaly detection, anomaly model wrappers around forecasts/filters, score alignment, or anomaly evaluation boundaries.
references/workflows.md for scorer + detector patterns, window effects, and forecasting residual wrappers.references/api-reference.md for key scorer/detector classes and verified constructor notes.references/troubleshooting.md for window length, fitting order, score-vs-binary confusion, and label alignment.scripts/anomaly_smoke.py for a tiny KMeansScorer + QuantileDetector smoke.KMeansScorer or another Darts scorer, fit on normal training data where required, then score validation/test.QuantileDetector, ThresholdDetector, or IQRDetector on normal score behavior, then detect validation scores.ForecastingAnomalyModel or related wrapper around a Darts forecasting model; route model training to ../forecasting-workflows/ or ../torch-and-foundation-models/.../evaluation-and-explainability/ when needed.python scripts/anomaly_smoke.py --json
The smoke uses generated normal/spike series and asserts scorer/detector output types, binary values, and window-shortened score length.
This sub-skill does not own time-series construction, forecasting model selection, or metric aggregation. It owns Darts anomaly API semantics and alignment. Do not treat anomaly scores as calibrated probabilities unless the chosen scorer/detector explicitly supports that interpretation.