Can this skill help with software performance tuning?

Yes, it is specifically designed to handle 'screening' where you might have dozens of configuration parameters and need to find the 'vital few' that actually impact performance.

When should I use DOE instead of standard A/B testing?

Use DOE when you have more than two variables or suspect that factors might interact (e.g., the effect of a button color depends on the background layout).

What is the Design of Experiments (DOE) skill?

DOE is a statistical framework that helps you plan, execute, and analyze experiments to understand how multiple variables affect an outcome with minimal testing effort.

How does this skill improve experimental budget management?

By using structured designs like fractional factorials, it allows you to test many factors simultaneously in fewer runs than testing variables one-by-one.

Does this skill support response surface methodology?

Yes, it provides guidance and templates for Central Composite and Box-Behnken designs to help you map curvature and find precise mathematical optima.

Design of Experiments

Name: Design of Experiments
Author: lyndonkl

bylyndonkl

•

データサイエンスとML

Optimizes complex multi-factor systems through systematic statistical experimental design and structured parameter tuning.

概要

Design of Experiments (DOE) is a statistical framework that enables users to identify how multiple input factors influence output responses while minimizing the total number of experimental runs. This skill provides Claude with structured workflows for screening numerous variables, discovering hidden interactions, and mapping response surfaces to find peak performance levels. It is an essential tool for software performance tuning, machine learning hyperparameter optimization, and complex A/B/n testing where traditional one-factor-at-a-time testing is inefficient or misleading.

主な機能

Discovery of complex interaction effects where parameters influence each other
Systematic factor screening to identify critical variables from large parameter sets
Response surface mapping to visualize performance landscapes and find optima
8 GitHub stars
Robustness validation to ensure system performance remains stable against noise
Optimization of multi-factor settings using full and fractional factorial designs

ユースケース

Tuning machine learning hyperparameters using structured statistical methodologies
Optimizing software configuration parameters for maximum throughput or minimal latency
Designing complex web experiments with multiple interacting layout and content variables

概要

主な機能

Discovery of complex interaction effects where parameters influence each other
Systematic factor screening to identify critical variables from large parameter sets
Response surface mapping to visualize performance landscapes and find optima
8 GitHub stars
Robustness validation to ensure system performance remains stable against noise
Optimization of multi-factor settings using full and fractional factorial designs

ユースケース

Tuning machine learning hyperparameters using structured statistical methodologies
Optimizing software configuration parameters for maximum throughput or minimal latency
Designing complex web experiments with multiple interacting layout and content variables