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.
View on GitHubskills/design-of-experiments/SKILL.md
January 24, 2026
Select agents to install to:
npx add-skill https://github.com/lyndonkl/claude/blob/main/skills/design-of-experiments/SKILL.md -a claude-code --skill design-of-experimentsInstallation paths:
.claude/skills/design-of-experiments/# Design of Experiments ## Table of Contents - [Purpose](#purpose) - [When to Use](#when-to-use) - [What Is It?](#what-is-it) - [Workflow](#workflow) - [Common Patterns](#common-patterns) - [Guardrails](#guardrails) - [Quick Reference](#quick-reference) ## Purpose 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: - **Screen** many factors to find the critical few - **Optimize** factor settings to maximize/minimize a response - **Discover interactions** where factors affect each other - **Map response surfaces** to understand the full factor space - **Validate robustness** against noise and environmental variation ## When to Use Use this skill when: - **Limited experimental budget**: You have constraints on time, cost, or resources for testing - **Multiple factors**: 3+ controllable variables that could affect the outcome - **Interaction suspicion**: Factors may interact (effect of A depends on level of B) - **Optimization needed**: Finding best settings, not just "better than baseline" - **Screening required**: Many candidate factors (10+), need to identify vital few - **Response surface**: Need to map curvature, find peaks/valleys, understand tradeoffs - **Robust design**: Must work well despite noise factors or environmental variation - **Process improvement**: Manufacturing, chemical processes, software performance tuning - **Product development**: Formulations, recipes, configurations with multiple parameters - **A/B/n testing**: Web/app features with multiple variants and combinations - **Machine learning**: Hyperparameter tuning for models with many parameters Trigger phrases: "optimize", "tune parameters", "factorial test", "interaction effects", "response surface", "efficient experiments", "minimize runs", "robustness