Return code: 0
Duration: 103.5s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: import clingo
  python_exec: asp_program = """
  python_exec: defining function solve_car_scheduling()
  python_exec: print(json.dumps(solution, indent=2))
  python_exec: defining function verify_solution()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': "Task completed succe...


                                Task Complete! ✓                                

I've successfully solved the car assembly line scheduling problem using Answer  
Set Programming. Here's what was accomplished:                                  

                                Solution Summary                                

The ASP model found a valid sequence that satisfies all 6 constraint types:     

 1 Assignment: All 12 positions filled with correct car type counts (A:3, B:3,  
   C:4, D:2)                                                                    
 2 Hierarchical Options: Option 5 (Panoramic Sunroof) treated as implying Option
   1 for capacity checks                                                        
 3 Positional Ban: No EV Powertrain (Option 4) at positions 1 or 12 ✓           
 4 Equipment Cooldown: Custom Paint (Option 2) cars have at least 2-slot gaps ✓ 
 5 Standard Capacity: At most 2 sunroof cars in any 4-consecutive window ✓      
 6 Conditional Capacity: Sport Package capacity adjusts based on preceding EV ✓ 

                          Key Implementation Features                           

 • Efficient modeling: Used derived predicates and aggregates to avoid grounding
   explosion                                                                    
 • Constrained at source: Choice rules only generate valid assignments          
 • Fast solving: Solution found in <1 second by limiting to first valid answer  
 • Verified correctness: All constraints programmatically validated             

The final code has been saved to 037_car_sequencing_hard_task_code.py and       
outputs the solution in the required JSON format.                               

==================================================
📊 Execution Statistics
==================================================

🔧 Tool Usage:
  python_exec            5 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        168,085
  Output tokens:       4,837
  Total tokens:        172,922

⏱️  Execution time: 1m 41.9s
==================================================


=== STDERR ===
