Babysitter doe-optimizer
Skill for optimizing experimental designs using DOE principles
install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/scientific-discovery/skills/doe-optimizer" ~/.claude/skills/a5c-ai-babysitter-doe-optimizer && rm -rf "$T"
manifest:
library/specializations/domains/science/scientific-discovery/skills/doe-optimizer/SKILL.mdsource content
DOE Optimizer Skill
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