data-science-plugin

Scikit-learn API patterns for preprocessing, pipelines, model selection, and evaluation. Use when /ds:experiment needs to build sklearn pipelines, tune hyperparameters, or evaluate models.

Check Python environment for required DS/ML libraries and report versions or missing packages. Use when setting up a new project or debugging import errors.

Hyperparameter tuning workflow reference -- strategy selection, Bayesian optimization with Optuna, search space design, and result analysis. Use when /ds:experiment needs to choose a tuning strategy, design search spaces, or analyze tuning runs.

Pre-model data preparation pipelines for cleaning, validation, transformation, and ETL orchestration. Use when raw data needs deduplication, schema validation, format conversion, or quality assurance before EDA or modeling.

Pandas API patterns for DataFrame operations, data cleaning, aggregation, merging, and performance optimization. Use when generating pandas code for data loading, manipulation, or profiling in /ds:eda, /ds:preprocess, or /ds:experiment.

Polars expression API for high-performance DataFrame operations, lazy evaluation, joins, aggregations, and I/O. Use as a parallel alternative to pandas-pro when working with large datasets or generating Polars code for data loading, manipulation, or profiling in /ds:eda, /ds:preprocess, or /ds:experiment.

Data quality validation with Great Expectations, dbt tests, and data contracts. Use when building formal validation rules, expectation suites, or data contracts for repeatable quality gates.

Detect file types and perform format-specific EDA across 200+ scientific data formats. Use when /ds:eda encounters non-tabular or unfamiliar data files, or when format-specific analysis guidance is needed.

SHAP API patterns for model interpretability -- explainer selection, feature attribution, and visualization. Use when /ds:experiment needs per-prediction explanations, global feature importance, or interaction analysis. For built-in tree importance and permutation importance use scikit-learn; for coefficient-based interpretation use statsmodels.

Guided statistical analysis with test selection, assumption checking, power analysis, and APA reporting. Use when /ds:experiment needs to design comparison protocols, validate assumptions, or report results.

Systematic exploratory data analysis checklist covering structure, quality, distributions, relationships, and target analysis. Use when starting EDA on any dataset.

Generate standardized model documentation following HuggingFace Model Card and NVIDIA Model Card++ formats. Use when preparing a model for deployment or handoff.

Verify that an ML experiment meets reproducibility requirements: random seeds, library versions, data hashes, environment capture. Use when reviewing experiments before shipping.

Aeon API patterns for time series machine learning -- classification, regression, clustering, anomaly detection, segmentation, and similarity search. Use when /ds:experiment needs time-series-specific ML algorithms (ROCKET, InceptionTime, DTW classifiers), or /ds:eda needs temporal feature extraction (Catch22, ROCKET features) or change point detection. For classical statistical forecasting (ARIMA/SARIMAX) use statsmodels; for tabular ML pipelines use scikit-learn; for visualization use matplotlib.

Statsmodels API patterns for OLS, GLM, discrete choice, time series (ARIMA/SARIMAX), and diagnostics. Use when /ds:experiment needs statsmodels model fitting, diagnostics, or time-series forecasting, or /ds:eda needs VIF and stationarity checks. For guided test selection and APA reporting use statistical-analysis.

Matplotlib API patterns for creating publication-quality visualizations. Use when /ds:eda needs distribution plots, correlation heatmaps, or relationship visualizations, or when /ds:experiment needs result plots (learning curves, confusion matrices, forecast visualizations). For standard ML diagnostic plots use scikit-learn display utilities; for statsmodels diagnostic plots use statsmodels built-in plotting; for quick statistical plots prefer seaborn.

Standard format for logging ML experiments including hypothesis, config, results, and learnings. Use when running experiments to maintain a consistent record.

Select and implement appropriate train/validation/test split strategies based on data characteristics. Use when designing the evaluation framework for a model.

Detect target leakage in feature sets by checking temporal validity, feature-target correlation, and information flow. Use before training any model.