doe-optimizer
Skill for optimizing experimental designs using DOE principles
npx skills add a5c-ai/babysitter --skill doe-optimizer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Purpose
- Capabilities
- Usage Guidelines
- Process Integration
- Configuration
- Output Artifacts
What does the doe-optimizer skill do?
Skill for optimizing experimental designs using DOE principles
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill doe-optimizer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.