Experiment Planner DOE
SkillMediaDesign of Experiments skill for systematic optimization of nanomaterial synthesis and processing
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Experiment Planner DOE skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/nanotechnology/skills/experiment-planner-doe/SKILL.md and read by ahel’s review.
Purpose
The Experiment Planner DOE skill provides systematic experimental design for nanomaterial synthesis and processing optimization, enabling efficient exploration of parameter space and robust process development.
Capabilities
- Factorial design generation
- Response surface methodology
- Taguchi method implementation
- ANOVA analysis
- Optimization predictions
- Robustness testing
Usage Guidelines
DOE Workflow
-
Design Selection
- Identify factors and levels
- Choose appropriate design
- Calculate required runs
-
Execution Planning
- Randomize run order
- Include replicates
- Plan blocking if needed
-
Analysis
- Perform ANOVA
- Build response models
- Optimize parameters
Process Integration
- Nanoparticle Synthesis Protocol Development
- Thin Film Deposition Process Optimization
- Nanolithography Process Development
Input Schema
{
"factors": [{
"name": "string",
"low": "number",
"high": "number",
"type": "continuous|categorical"
}],
"responses": ["string"],
"design_type": "factorial|fractional|rsm|taguchi",
"constraints": {
"max_runs": "number",
"blocking": "boolean"
}
}
Output Schema
{
"design": {
"type": "string",
"runs": "number",
"run_table": [{
"run": "number",
"factors": {},
"block": "number"
}]
},
"analysis": {
"anova_table": {},
"significant_factors": ["string"],
"r_squared": "number"
},
"optimization": {
"optimal_settings": {},
"predicted_response": "number",
"confidence_interval": {"lower": "number", "upper": "number"}
}
}
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
experiment-planner-doe- Source
- github.com/a5c-ai/babysitter