This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
8.3
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0
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Machine Learning
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Excellent skill with comprehensive coverage of time series ML tasks. The SKILL.md provides clear, actionable guidance with code examples for all major use cases (classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search). Well-structured with quick-start examples, algorithm selection guides, best practices, and clean references to detailed documentation. The skill is highly novel - time series analysis requires specialized algorithms (DTW, ROCKET, STOMP) that would consume significant tokens if implemented from scratch via CLI. Minor room for improvement: could add more explicit input format validation examples and error handling patterns. The logical separation of detailed algorithms into reference files maintains excellent readability while ensuring comprehensive coverage. Strong practical value for temporal data analysis workflows.
K-Dense-AI
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