DOE Optimizer Skill

SkillDev tools

Skill for optimizing experimental designs using DOE principles

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the DOE Optimizer Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/scientific-discovery/skills/doe-optimizer/SKILL.md and read by ahel’s review.

Purpose

Optimize experimental designs using Design of Experiments (DOE) principles for efficient factor screening and response optimization.

Capabilities

Create factorial designs
Generate fractional factorials
Build response surface designs
Optimize factor levels
Analyze design properties
Generate run orders

Usage Guidelines

Define factors and levels
Select design type
Generate design matrix
Analyze properties
Optimize if needed
Plan execution order

Process Integration

Works within scientific discovery workflows for:

Process optimization
Factor screening
Response modeling
Efficient experimentation

Configuration

Design type selection
Factor specifications
Resolution requirements
Optimization criteria

Output Artifacts

Design matrices
Run order lists
Property analyses
Optimization results

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: doe-optimizer
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter