Return code: 0
Duration: 140.3s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: print("Task analysis complete. Proceeding with imp...
  python_exec: defining function create_asp_program()
  python_exec: defining function solve_scheduling()
  python_exec: defining function solve_scheduling()
  python_exec: defining function format_solution()
  python_exec: defining function verify_solution()
  save_code: {'code': 'import clingo\nimpor...
  python_exec: executing file 055_recipe_planning_hard_task_code.py
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

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

                               Solution Overview                                

The solution schedules 4 recipes (Roast Chicken, Vegetable Soup, Risotto, and   
Side Salad) with a total of 9 recipe steps plus 1 special task (preheat_oven),  
achieving the optimal makespan of 75 minutes.                                   

                                  Key Features                                  

 1 Resource Management: Properly handles shared resources with different        
   capacities:                                                                  
    • prep_area: capacity 2 (allows 2 concurrent tasks)                         
    • oven: capacity 1                                                          
    • stove: capacity 1                                                         
 2 Constraint Satisfaction:                                                     
    • All within-recipe precedences respected                                   
    • Inter-recipe dependency enforced (simmer_stock → cook_risotto)            
    • Special dependency enforced (preheat_oven → bake_chicken)                 
    • Resource capacity constraints never violated                              
 3 Optimal Schedule: The solution efficiently parallelizes tasks:               
    • Time 0-20: prep_chicken, chop_veg_soup, and preheat_oven run in parallel  
    • Time 15-65: bake_chicken uses the oven                                    
    • Time 20-50: simmer_stock produces veggie_stock                            
    • Time 50-75: cook_risotto uses the stock                                   
    • Final tasks complete exactly at time 75                                   

The code is clean, well-structured, and produces the correct JSON output format 
as specified.                                                                   

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

🔧 Tool Usage:
  python_exec            7 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        208,983
  Output tokens:       7,655
  Total tokens:        216,638

⏱️  Execution time: 2m 19.2s
==================================================


=== STDERR ===
