每个推荐都保留与其仓库、审计和安装路径的明确关联。
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.
Analyze gza run logs to find recurring anti-patterns, wasted effort, and suggest AGENTS.md or workflow improvements
每个推荐都保留与其仓库、审计和安装路径的明确关联。
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.
Analyze gza run logs to find recurring anti-patterns, wasted effort, and suggest AGENTS.md or workflow improvements
每个推荐都保留与其仓库、审计和安装路径的明确关联。
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.
Analyze gza run logs to find recurring anti-patterns, wasted effort, and suggest AGENTS.md or workflow improvements
每个推荐都保留与其仓库、审计和安装路径的明确关联。
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.
Analyze gza run logs to find recurring anti-patterns, wasted effort, and suggest AGENTS.md or workflow improvements