doe-optimizer

Solid

Skill for optimizing experimental designs using DOE principles

AI & Automation 814 stars 53 forks Updated today MIT

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Skill 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 1. Define factors and levels 2. Select design type 3. Generate design matrix 4. Analyze properties 5. Optimize if needed 6. 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

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

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