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Experiment planner doe

Skill a5c-ai/babysitter/library/specializations/domains/science/nanotechnology/skills/experiment-planner-doe

Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration

Install
npx -y skills add a5c-ai/babysitter --skill experiment-planner-doe

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Design of Experiments skill for systematic optimization of nanomaterial synthesis and processing

SKILL.md

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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

  1. Design Selection

    • Identify factors and levels
    • Choose appropriate design
    • Calculate required runs
  2. Execution Planning

    • Randomize run order
    • Include replicates
    • Plan blocking if needed
  3. 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"}
  }
}

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