Babysitter experiment-planner-doe
Design of Experiments skill for systematic optimization of nanomaterial synthesis and processing
install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/nanotechnology/skills/experiment-planner-doe" ~/.claude/skills/a5c-ai-babysitter-experiment-planner-doe && rm -rf "$T"
manifest:
library/specializations/domains/science/nanotechnology/skills/experiment-planner-doe/SKILL.mdsource content
Experiment Planner DOE
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"} } }