# Analyze Optimization Workflow Skill

You are analyzing a structural optimization request for the Atomizer system.

When the user provides a request, break it down into atomic workflow steps and classify each step intelligently.

## Step Types

**1. ENGINEERING FEATURES** - Complex FEA/CAE operations needing specialized knowledge:
- Extract results from OP2 files (displacement, stress, strain, element forces, etc.)
- Modify FEA properties (CBUSH/CBAR stiffness, PCOMP layup, material properties)
- Run simulations (SOL101, SOL103, etc.)
- Create/modify geometry in NX

**2. INLINE CALCULATIONS** - Simple math operations (auto-generate Python):
- Calculate average, min, max, sum
- Compare values, compute ratios
- Statistical operations

**3. POST-PROCESSING HOOKS** - Custom calculations between FEA steps:
- Custom objective functions combining multiple results
- Data transformations
- Filtering/aggregation logic

**4. OPTIMIZATION** - Algorithm and configuration:
- Optuna, genetic algorithm, etc.
- Design variables and their ranges
- Multi-objective vs single objective

## Important Distinctions

- "extract forces from 1D elements" → ENGINEERING FEATURE (needs pyNastran/OP2 knowledge)
- "find average of forces" → INLINE CALCULATION (simple Python: sum/len)
- "compare max to average and create metric" → POST-PROCESSING HOOK (custom logic)
- Element forces vs Reaction forces are DIFFERENT (element internal forces vs nodal reactions)
- CBUSH vs CBAR are different element types with different properties
- Extract from OP2 vs Read from .prt expression are different domains

## Output Format

Return a detailed JSON analysis with this structure:

```json
{
  "engineering_features": [
    {
      "action": "extract_1d_element_forces",
      "domain": "result_extraction",
      "description": "Extract element forces from 1D elements (CBAR) in Z direction from OP2 file",
      "params": {
        "element_types": ["CBAR"],
        "result_type": "element_force",
        "direction": "Z"
      },
      "why_engineering": "Requires pyNastran library and OP2 file format knowledge"
    }
  ],
  "inline_calculations": [
    {
      "action": "calculate_average",
      "description": "Calculate average of extracted forces",
      "params": {
        "input": "forces_z",
        "operation": "mean"
      },
      "code_hint": "avg = sum(forces_z) / len(forces_z)"
    },
    {
      "action": "find_minimum",
      "description": "Find minimum force value",
      "params": {
        "input": "forces_z",
        "operation": "min"
      },
      "code_hint": "min_val = min(forces_z)"
    }
  ],
  "post_processing_hooks": [
    {
      "action": "custom_objective_metric",
      "description": "Compare minimum to average and create objective metric to minimize",
      "params": {
        "inputs": ["min_force", "avg_force"],
        "formula": "min_force / avg_force",
        "objective": "minimize"
      },
      "why_hook": "Custom business logic that combines multiple calculations"
    }
  ],
  "optimization": {
    "algorithm": "genetic_algorithm",
    "design_variables": [
      {
        "parameter": "cbar_stiffness_x",
        "type": "FEA_property",
        "element_type": "CBAR",
        "direction": "X"
      }
    ],
    "objectives": [
      {
        "type": "minimize",
        "target": "custom_objective_metric"
      }
    ]
  },
  "summary": {
    "total_steps": 5,
    "engineering_needed": 1,
    "auto_generate": 4,
    "research_needed": ["1D element force extraction", "Genetic algorithm implementation"]
  }
}
```

Be intelligent about:
- Distinguishing element types (CBUSH vs CBAR vs CBEAM)
- Directions (X vs Y vs Z)
- Metrics (min vs max vs average)
- Algorithms (Optuna TPE vs genetic algorithm vs gradient-based)
- Data sources (OP2 file vs .prt expression vs .fem file)

Return ONLY the JSON analysis, no other text.