andikarachman

andikarachman/data-science-plugin

Plugin Claude Code

Plugin marketplace listing 1 plugin: ds.

andikarachman/data-science-plugin

Plugin Claude Code

Data science and ML workflow tools. 9 agents, 8 commands, 19 skills, 9 templates for problem framing, preprocessing, validation, EDA, experimentation, review, deployment, and knowledge compounding.

andikarachman/data-science-plugin

Instructions file

Claude Code instructions for andikarachman/data-science-plugin, covering claude.md, project overview, plugin structure, versioning & component count sync and invocation map.

andikarachman/data-science-plugin

Command

Extract and categorize learnings from completed experiments into docs/ds/learnings/ for future retrieval.

andikarachman/data-science-plugin

Command

Profile a dataset for structure, quality, distributions, and anomalies, then output an EDA report.

andikarachman/data-science-plugin

Command

Design an ML experiment with hypothesis, split strategy, leakage check, and evaluation plan.

andikarachman/data-science-plugin

Command

Frame a data science problem and plan the approach, surfacing relevant past learnings.

andikarachman/data-science-plugin

Command

Clean, validate, and transform raw data using automated preprocessing pipelines.

andikarachman/data-science-plugin

Command

Peer review an ML experiment for methodology, leakage, reproducibility, and statistical validity.

andikarachman/data-science-plugin

Command

Assess deployment readiness of a trained model and generate model card and deployment documentation.

andikarachman/data-science-plugin

Command

Run data quality validation using formal expectation suites, dbt tests, or data contracts.

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.