Atomizer/.claude/skills/guided-study-wizard.md

Guided Study Creation Wizard

Version: 1.0 Purpose: Interactive conversational wizard for creating new optimization studies from scratch.

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Overview

This skill provides a step-by-step guided experience for users who want to create a new optimization study. It asks focused questions to gather requirements, then generates the complete study configuration.

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Wizard Flow

Phase 1: Understanding the Problem (Discovery)

Start with open-ended questions to understand what the user wants to optimize:

Opening Prompt:

I'll help you set up a new optimization study. Let's start with the basics:

1. **What are you trying to optimize?**
   - Describe the physical system (e.g., "a telescope mirror", "a UAV arm", "a bracket")

2. **What's your goal?**
   - Minimize weight? Maximize stiffness? Minimize stress? Multiple objectives?

3. **Do you have an NX model ready?**
   - If yes, where is it located?
   - If no, we can discuss what's needed

Phase 2: Model Analysis (If NX model provided)

If user provides a model path:

1. Check the model exists

   # Verify path
   model_path = Path(user_provided_path)
   if model_path.exists():
       # Proceed with analysis
   else:
       # Ask for correct path
   

2. Extract expressions (design parameters)

   • List all NX expressions that could be design variables
   • Ask user to confirm which ones to optimize

3. Identify simulation setup

   • What solution types are present? (static, modal, buckling)
   • What results are available?

Phase 3: Define Objectives & Constraints

Ask focused questions:

Based on your model, I can see these results are available:
- Displacement (from static solution)
- Von Mises stress (from static solution)
- Natural frequency (from modal solution)
- Mass (from geometry)

**Questions:**

1. **Primary Objective** - What do you want to minimize/maximize?
   Examples: "minimize tip displacement", "minimize mass"

2. **Secondary Objectives** (optional) - Any other goals?
   Examples: "also minimize stress", "maximize first frequency"

3. **Constraints** - What limits must be respected?
   Examples: "stress < 200 MPa", "frequency > 50 Hz", "mass < 2 kg"

Phase 4: Define Design Space

For each design variable identified:

For parameter `{param_name}` (current value: {current_value}):
- **Minimum value**: (default: -20% of current)
- **Maximum value**: (default: +20% of current)
- **Type**: continuous or discrete?

Phase 5: Optimization Settings

**Optimization Configuration:**

1. **Number of trials**: How thorough should the search be?
   - Quick exploration: 50-100 trials
   - Standard: 100-200 trials
   - Thorough: 200-500 trials
   - With neural acceleration: 500+ trials

2. **Protocol Selection** (I'll recommend based on your setup):
   - Single objective → Protocol 10 (IMSO)
   - Multi-objective (2-3 goals) → Protocol 11 (NSGA-II)
   - Large-scale with NN → Protocol 12 (Hybrid)

3. **Neural Network Acceleration**:
   - Enable if n_trials > 100 and you want faster iterations

Phase 6: Summary & Confirmation

Present the complete configuration for user approval:

## Study Configuration Summary

**Study Name**: {study_name}
**Location**: studies/{study_name}/

**Model**: {model_path}

**Design Variables** ({n_vars} parameters):
| Parameter | Min | Max | Type |
|-----------|-----|-----|------|
| {name1} | {min1} | {max1} | continuous |
| ... | ... | ... | ... |

**Objectives**:
- {objective1}: {direction1}
- {objective2}: {direction2} (if multi-objective)

**Constraints**:
- {constraint1}
- {constraint2}

**Settings**:
- Protocol: {protocol}
- Trials: {n_trials}
- Sampler: {sampler}
- Neural Acceleration: {enabled/disabled}

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Does this look correct?
- Type "yes" to generate the study files
- Type "change X" to modify a specific setting
- Type "start over" to begin again

Phase 7: Generation

Once confirmed, generate:

1. Create study directory structure
2. Copy model files to working directory
3. Generate optimization_config.json
4. Generate run_optimization.py
5. Validate everything works

✓ Study created successfully!

**Next Steps:**
1. Review the generated files in studies/{study_name}/
2. Run a quick validation: `python run_optimization.py --validate`
3. Start optimization: `python run_optimization.py --start`

Or just tell me "start the optimization" and I'll handle it!

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Question Templates

For Understanding Goals

• "What problem are you trying to solve?"
• "What makes a 'good' design for your application?"
• "Are there any hard limits that must not be exceeded?"
• "Is this a weight reduction study, a performance study, or both?"

For Design Variables

• "Which dimensions or parameters should I vary?"
• "Are there any parameters that must stay fixed?"
• "What are reasonable bounds for {parameter}?"
• "Should {parameter} be continuous or discrete (specific values only)?"

For Constraints

• "What's the maximum stress this component can handle?"
• "Is there a minimum stiffness requirement?"
• "Are there weight limits?"
• "What frequency should the structure avoid (resonance concerns)?"

For Optimization Settings

• "How much time can you allocate to this study?"
• "Do you need a quick exploration or thorough optimization?"
• "Is this a preliminary study or final optimization?"

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Default Configurations by Use Case

Structural Weight Minimization

{
  "objectives": [
    {"name": "mass", "direction": "minimize", "target": null}
  ],
  "constraints": [
    {"name": "max_stress", "type": "<=", "value": 200e6, "unit": "Pa"},
    {"name": "max_displacement", "type": "<=", "value": 0.001, "unit": "m"}
  ],
  "n_trials": 150,
  "sampler": "TPE"
}

Multi-Objective (Weight vs Performance)

{
  "objectives": [
    {"name": "mass", "direction": "minimize"},
    {"name": "max_displacement", "direction": "minimize"}
  ],
  "n_trials": 200,
  "sampler": "NSGA-II"
}

Modal Optimization (Frequency Tuning)

{
  "objectives": [
    {"name": "first_frequency", "direction": "maximize"}
  ],
  "constraints": [
    {"name": "mass", "type": "<=", "value": 5.0, "unit": "kg"}
  ],
  "n_trials": 150,
  "sampler": "TPE"
}

Telescope Mirror (Zernike WFE)

{
  "objectives": [
    {"name": "filtered_rms", "direction": "minimize", "unit": "nm"}
  ],
  "constraints": [
    {"name": "mass", "type": "<=", "value": null}
  ],
  "extractor": "ZernikeExtractor",
  "n_trials": 200,
  "sampler": "NSGA-II"
}

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Error Handling

Model Not Found

I couldn't find a model at that path. Let's verify:
- Current directory: {cwd}
- You specified: {user_path}

Could you check the path and try again?
Tip: Use an absolute path like "C:/Users/.../model.prt"

No Expressions Found

I couldn't find any parametric expressions in this model.

For optimization, we need parameters defined as NX expressions.
Would you like me to explain how to add expressions to your model?

Invalid Constraint

That constraint doesn't match any available results.

Available results from your model:
- {result1}
- {result2}

Which of these would you like to constrain?

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Integration with Dashboard

When running from the Atomizer dashboard with a connected Claude terminal:

1. No study selected → Offer to create a new study
2. Study selected → Use that study's context, offer to modify or run

The dashboard will display the study once created, showing real-time progress.

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Quick Commands

For users who know what they want:

• create study {name} from {model_path} - Skip to model analysis
• quick setup - Use all defaults, just confirm
• copy study {existing} as {new} - Clone an existing study as starting point

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Remember

• Be conversational - This is a wizard, not a form
• Offer sensible defaults - Don't make users specify everything
• Validate as you go - Catch issues early
• Explain decisions - Say why you recommend certain settings
• Keep it focused - One question at a time, don't overwhelm