DOE Optimizer Skill
SkillDev toolsSkill for optimizing experimental designs using DOE principles
Use DOE Optimizer Skill in Claude, ChatGPT or Ahel Desktop
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Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
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
- 113
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
doe-optimizer- Source
- github.com/a5c-ai/babysitter