design-of-experiments

SKILL.md

Design of Experiments (DOE) helps you systematically discover how multiple factors affect an outcome while minimizing the number of experimental runs. Instead of testing one variable at a time (inefficient) or guessing randomly (unreliable), DOE uses structured experimental designs to:

Trigger phrases: "optimize", "tune parameters", "factorial test", "interaction effects", "response surface", "efficient experiments", "minimize runs", "robustness", "sensitivity analysis"

Design of Experiments is a statistical framework for planning, executing, and analyzing experiments where you deliberately vary multiple input factors to observe effects on output responses.

Use when optimizing multi-factor systems with limited experimental budget, screening many variables to find the vital few, discovering interactions between parameters, mapping response surfaces for peak performance, validating robustness to noise factors, or when users mention factorial designs, A/B/n testing, parameter tuning, process optimization, or experimental efficiency. Source: lyndonkl/claude.

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