Best use case
doe-optimizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Skill for optimizing experimental designs using DOE principles
Teams using doe-optimizer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/doe-optimizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How doe-optimizer Compares
| Feature / Agent | doe-optimizer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Skill for optimizing experimental designs using DOE principles
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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