andikarachman/data-science-plugin
19 skills
$npx skills add andikarachman/data-science-pluginSkillInstalls
aeonAeon API patterns for time series machine learning -- classification, regression, clustering, anomaly detection, segmentation, and similarity search.data-preprocessingPre-model data preparation pipelines for cleaning, validation, transformation, and ETL orchestration.data-quality-frameworksData quality validation with Great Expectations, dbt tests, and data contracts.eda-checklistSystematic exploratory data analysis checklist covering structure, quality, distributions, relationships, and target analysis.experiment-trackingStandard format for logging ML experiments including hypothesis, config, results, and learnings. Use when running experiments to maintain a consistent record.exploratory-data-analysisDetect file types and perform format-specific EDA across 200+ scientific data formats.matplotlibMatplotlib API patterns for creating publication-quality visualizations.model-cardGenerate standardized model documentation following HuggingFace Model Card and NVIDIA Model Card++ formats.pandas-proPandas API patterns for DataFrame operations, data cleaning, aggregation, merging, and performance optimization.polarsPolars expression API for high-performance DataFrame operations, lazy evaluation, joins, aggregations, and I/O.reproducibility-checklistVerify that an ML experiment meets reproducibility requirements: random seeds, library versions, data hashes, environment capture.scikit-learnScikit-learn API patterns for preprocessing, pipelines, model selection, and evaluation.setupCheck Python environment for required DS/ML libraries and report versions or missing packages. Use when setting up a new project or debugging import errors.shapSHAP API patterns for model interpretability -- explainer selection, feature attribution, and visualization.split-strategySelect and implement appropriate train/validation/test split strategies based on data characteristics. Use when designing the evaluation framework for a model.statistical-analysisGuided statistical analysis with test selection, assumption checking, power analysis, and APA reporting.statsmodelsStatsmodels API patterns for OLS, GLM, discrete choice, time series (ARIMA/SARIMAX), and diagnostics.target-leakage-detectionDetect target leakage in feature sets by checking temporal validity, feature-target correlation, and information flow. Use before training any model.tuning-hyperparametersHyperparameter tuning workflow reference -- strategy selection, Bayesian optimization with Optuna, search space design, and result analysis.