frontend
claudeDomain-modular React apps (Vite, React Router, RTK Query, shadcn/ui, Vitest), with thin architecture adapters for Vue and Angular.
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0
claude
kaizen-data-science
Data science on demand: exploration and quality checks, statistical analysis, modeling (predictive, forecasting, Bayesian, optimization), matplotlib/seaborn charts, and export to a reproducible Jupyter notebook in the Kaizen project structure.
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Plugin kaizen-data-science from marketplace kaizen-plugins in kaizenanalytix/kaizen-claude-plugins. Commands read from the repo's manifest — 1 month ago.
What it installs
Skills (5)
data-explorationExplore, understand, and quality-check a dataset and report findings. Use this skill whenever the user shares or references a dataset, dataframe, CSV/Excel/parquet file, or table and wants it inspected, profiled, summarized, assessed for quality, cleaned-assessed, or written up — including casual ph
modelingBuild and evaluate predictive and prescriptive models — regression and classification (scikit-learn, XGBoost, LightGBM, statsmodels), forecasting (ARIMA/SARIMAX, Holt-Winters, Prophet), Bayesian inference (conjugate/analytic and PyMC), and optimization (linear, mixed-integer, and non-linear programs
project-exportPackage the analysis from this conversation into a reproducible Jupyter notebook inside the mandatory Kaizen Analytix data-science project structure. Use this skill when the user asks — at any point after some analysis has been done (data understanding, statistics, and/or modeling) — to "export this
statistical-analysisAnalyze distributions and relationships, run hypothesis tests, segment or cluster data, and analyze time series. Use this skill when the user wants to examine how a variable is distributed, whether variables relate or correlate, test a hypothesis or compare groups, divide data into segments/clusters
visualizationCreate code-generated visualizations — histograms, box/violin plots, scatter plots, correlation heatmaps, count/bar charts, time-series lines, missingness charts, and model diagnostics (ROC/PR curves, confusion matrix, residuals, feature-importance bars, forecast plots) — using matplotlib and seabor
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