Data science and ML workflow tools for Claude Code. 6 agents, 4 commands, 6 skills for problem framing, EDA, experimentation, and knowledge compounding.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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…
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.
andikarachman/data-science-plugin
Skill Claude CodeCodex
Systematic exploratory data analysis checklist covering structure, quality, distributions, relationships, and target analysis. Use when starting EDA on any dataset.
andikarachman/data-science-plugin
Skill Claude CodeCodex
Standard format for logging ML experiments including hypothesis, config, results, and learnings. Use when running experiments to maintain a consistent record.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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…
andikarachman/data-science-plugin
Skill Claude CodeCodex
Generate standardized model documentation following HuggingFace Model Card and NVIDIA Model Card++ formats. Use when preparing a model for deployment or handoff.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.
andikarachman/data-science-plugin
Skill Claude CodeCodex
Verify that an ML experiment meets reproducibility requirements: random seeds, library versions, data hashes, environment capture. Use when reviewing experiments before shipping.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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…
andikarachman/data-science-plugin
Skill Claude CodeCodex
Select and implement appropriate train/validation/test split strategies based on data characteristics. Use when designing the evaluation framework for a model.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.
andikarachman/data-science-plugin
Skill Claude CodeCodex
Detect target leakage in feature sets by checking temporal validity, feature-target correlation, and information flow. Use before training any model.
andikarachman/data-science-plugin
Skill Claude CodeCodex
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.