Atomizer/.claude/skills/modules/study-interview-mode.md

# Study Interview Mode Skill

## Purpose

This skill enables an intelligent interview-based approach to study creation. Instead of asking users to fill out complex configuration files, Claude guides them through a natural conversation to gather all necessary information for optimization study setup.

**This is now the DEFAULT mode for all study creation.**

## Triggers (DEFAULT for Study Creation)

This skill is automatically invoked when the user says ANY of:
- "create a study", "new study", "set up study"
- "create a study for my bracket"
- "optimize this", "optimize my model"
- "I want to minimize mass", "I want to reduce weight"
- Any study creation request

### Skip Interview (Manual Mode)

Only skip to manual mode when user explicitly requests:
- "skip interview", "quick setup", "manual config"
- Power users recreating known configurations

## Interview Flow

### Phase 1: Introspection
Before questions begin, automatically analyze the NX model:
```python
from optimization_engine.interview import StudyInterviewEngine

engine = StudyInterviewEngine(study_path)

# Run introspection first (if model available)
introspection = {
    "expressions": [...],  # From part introspection
    "materials": [...],    # From simulation
    "load_cases": [...],   # From simulation
    "model_path": "...",
    "sim_path": "..."
}

session = engine.start_interview(study_name, introspection=introspection)
```

### Phase 2: Problem Definition
Ask about the study's purpose and context:
- What are you trying to optimize?
- Describe your study in a few words

### Phase 3: Objectives
Determine optimization goals:
- Primary goal (minimize mass, stress, displacement, etc.)
- Secondary objectives (if any)
- Multi-objective or single-objective?

The ExtractorMapper automatically assigns extractors:
- Mass → E4 (BDF Mass) or E5 (CAD Mass)
- Displacement → E1
- Stress → E3
- Frequency → E2
- Zernike → E8, E9, E10

### Phase 4: Constraints
Define physical limits:
- Material-aware validation (checks against yield stress)
- Auto-suggests safety factors
- Detects anti-patterns (e.g., mass minimization without constraints)

### Phase 5: Design Variables
Select parameters to vary:
- Dynamic options from introspection
- Auto-suggests bounds based on current values
- Detects too-wide or too-narrow bounds

### Phase 6: Validation
Final checks before generation:
- Run baseline simulation (optional)
- Verify all parameters accessible
- Check for conflicting constraints

### Phase 7: Review
Present StudyBlueprint for confirmation:
- Show all settings in readable format
- Allow what-if modifications
- Confirm or restart

## Key Classes

### StudyInterviewEngine
Main orchestrator:
```python
from optimization_engine.interview import StudyInterviewEngine

engine = StudyInterviewEngine(study_path)
session = engine.start_interview(study_name, introspection=introspection)

# Get first question
action = engine.get_first_question()
# Present action.message to user

# Process user answer
next_action = engine.process_answer(user_response)

# When complete, get blueprint
if next_action.action_type == "show_summary":
    blueprint = next_action.blueprint
```

### InterviewState
Persisted interview state with JSON serialization:
```python
from optimization_engine.interview import InterviewState, InterviewStateManager

manager = InterviewStateManager(study_path)
state = manager.load_state()  # Resume if exists
```

### StudyBlueprint
Validated configuration ready for generation:
```python
blueprint = engine.generate_blueprint()
config = blueprint.to_config_json()  # For optimization_config.json
```

## Anti-Pattern Detection

The EngineeringValidator detects common mistakes:
- `mass_no_constraint`: Mass minimization without stress/displacement limits
- `stress_over_yield`: Stress constraint exceeds material yield
- `bounds_too_wide`: Design variable range > 100x
- `too_many_objectives`: More than 3 objectives
- `single_dv_many_trials`: Many trials for single variable

When detected, user is warned and asked to acknowledge.

## Materials Database

Built-in materials with properties:
- Aluminum alloys (6061-T6, 7075-T6)
- Steel grades (A36, 304 SS, 316 SS)
- Titanium (Ti-6Al-4V)
- Composites (CFRP, GFRP)
- Plastics (ABS, Nylon)

Fuzzy matching supports user input like "Al 6061", "aluminum", "6061-T6".

## Presenter Modes

### ClaudePresenter (Default)
Markdown-formatted for Claude conversation:
```markdown
### Question 1 of ~12: Problem Definition

What are you trying to optimize?

1. Minimize mass/weight
2. Minimize maximum stress
3. Minimize displacement
4. Maximize natural frequency
```

### DashboardPresenter
JSON events for React dashboard (future).

### CLIPresenter
Plain text for command-line interface (future).

## State Persistence

Interview state is saved after each answer:
```
studies/my_study/
├── .interview/
│   ├── state.json         # Current state
│   ├── state.lock         # Concurrency lock
│   ├── interview_log.md   # Audit trail
│   └── backups/           # State history
│       ├── state_20260102_103000.json
│       └── ...
```

Supports resume after session interruption.

## Integration with Study Wizard

After interview completes:
```python
from optimization_engine.study.wizard import StudyWizard

wizard = StudyWizard.from_interview(blueprint, study_path)
wizard.generate()
```

## Example Conversation

**Claude**: Let me help you set up your optimization study. I'll ask a few questions to understand what you're trying to achieve.

**Claude**: ### Question 1 of ~12: Problem Definition
What are you trying to optimize? (You can describe in your own words)

**User**: I want to make this bracket as light as possible

**Claude**: Got it - mass minimization. Let me check the material...

**Claude**: ### Question 2 of ~12: Constraints
Your model uses Aluminum 6061-T6 (yield stress: 276 MPa).
What's the maximum allowable stress? I recommend staying below 184 MPa (SF=1.5).

**User**: 200 MPa should be fine

**Claude**: I notice 200 MPa is close to the yield stress. This allows minimal plastic deformation.
Do you want to proceed with this value? [Yes/No]

**User**: Yes, proceed

*(Interview continues...)*

**Claude**: ### Summary
Here's your optimization setup:

| Setting | Value |
|---------|-------|
| Study Name | bracket_v1 |
| Objective | Minimize mass (E4) |
| Constraint | Max stress ≤ 200 MPa |
| Design Variables | thickness [1-10 mm], rib_height [5-20 mm] |
| Trials | 100 |
| Sampler | TPE |

Ready to generate? [Confirm/Modify/Restart]

## Files Created

After confirmation, the following are generated:
- `optimization_config.json` - From blueprint.to_config_json()
- `run_optimization.py` - From SYS_10 or SYS_11 template
- `INTERVIEW_LOG.md` - Audit trail of Q&A

## Error Handling

If the interview is interrupted:
1. State is auto-saved after each answer
2. On restart, detect existing state
3. Offer to resume or start fresh

If validation fails:
1. Present error with fix suggestion
2. Ask user to acknowledge or modify
3. Don't block on warnings, only on errors