---
name: experiment-planner-doe-9a3e1d6b2262
description: Design of Experiments skill for systematic optimization of nanomaterial synthesis and processing
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
metadata:
  specialization: nanotechnology
  domain: science
  category: infrastructure-quality
  priority: high
  phase: 6
  tools-libraries:
    - JMP
    - Design-Expert
    - Minitab
    - scipy.stats
graph:
  domains: [domain:nanotechnology]
  skillAreas: [skill-area:mathematical-reasoning, skill-area:physics-simulation, skill-area:data-analysis]
  workflows: [workflow:experiment-design]
  roles: [role:research-engineer]
---

# 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

```json
{
  "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

```json
{
  "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"}
  }
}
```


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Education & Research, Product Management
Roles: Data Scientist, Growth Product Manager, Product Manager, Researcher, Technical Product Manager
Specialties: Specialty not specified
Tools: Design Expert, Jmp, Minitab, Scipy.Stats
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 8542baa7216e2da87d38ca01d3f8305d6f16c9d96d6c8103e4852747bcbf6997