design-of-experiments

github:jkitchin/skillz · first committed 2025-11-29 · source · badge

Expert guidance for Design of Experiments (DOE) in Python - interactive goal-driven design selection, classical DOE (factorial, response surface, screening), Bayesian optimization with Gaussian processes, model-driven optimal designs, active learning, and sequential experimentation; includes pyDOE3, pycse, GPyOpt, scikit-optimize, statsmodels

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Listed, not yet scored. This skill has no public model test yet. It is in the scoring queue; submitted skills go first, then the most-installed.

Version history

2 commits to this file on GitHub (newest first).

2026-03-07 · commit 3e74b2f · Add CASCADE-inspired skill evolution commands and fix skill quality issues
2025-11-29 · commit e42851d · Add design-of-experiments skill for DOE in Python

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