Return code: 0
Duration: 93.0s

=== 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: defining function generate_asp_program()
  python_exec: defining function solve_meeting_schedule()
  python_exec: defining function verify_solution()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

I've successfully completed the meeting scheduling task using Answer Set        
Programming (ASP) with clingo. Here's what was accomplished:                    

                               Solution Overview                                

The ASP model found an optimal schedule with zero preference violations,        
scheduling all 5 meetings across 3 days:                                        

 • m1 (Project kickoff): Day 1, Slot 1, Room r1 ✓ (preferred time)              
 • m2 (Budget review): Day 1, Slot 2, Room r2 ✓ (preferred time)                
 • m3 (Technical design): Day 1, Slot 2, Room r1                                
 • m4 (Client presentation): Day 3, Slot 3, Room r1 ✓ (preferred time)          
 • m5 (Team retrospective): Day 2, Slot 3, Room r1                              

                                  Key Features                                  

 1 All constraints satisfied:                                                   
    • No person conflicts (e.g., m2 and m3 both at day 1, slot 2, but in        
      different rooms with no shared attendees)                                 
    • No room double-booking                                                    
    • Each meeting assigned exactly once                                        
 2 Optimal result: All 3 meetings with time preferences were scheduled at their 
   preferred times (0 violations)                                               
 3 Efficient ASP model:                                                         
    • Used choice rules for assignment generation                               
    • Integrity constraints for conflict prevention                             
    • Constraint-based optimization (expected optimal = 0)                      

The solution has been saved to 008_meeting_scheduling_easy_task_code.py and     
produces the required JSON output format.                                       

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

🔧 Tool Usage:
  python_exec            4 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        143,100
  Output tokens:       4,396
  Total tokens:        147,496

⏱️  Execution time: 1m 31.2s
==================================================


=== STDERR ===
