Return code: 0
Duration: 138.8s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: recipes_data = {
  python_exec: defining function generate_asp_facts()
  python_exec: defining function build_asp_program()
  python_exec: defining function solve_recipe_scheduling()
  python_exec: defining function build_resource_usage()
  python_exec: defining function verify_solution()
  python_exec: defining function generate_asp_facts()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'Task completed succe...


                         Task Completed Successfully! ✓                         

I've successfully solved the recipe scheduling problem using Answer Set         
Programming (ASP) with the clingo Python API. Here's what was accomplished:     

                               Solution Summary:                                

 • Total cooking time: 35 minutes (optimal)                                     
 • All 6 recipe steps scheduled without conflicts                               
 • Resource utilization: Efficient parallel execution where possible            

                            Key Schedule Highlights:                            

 1 Pasta prep (0-10 min) and Bread baking (0-30 min) start simultaneously       
 2 Pasta boiling (10-25 min) and Salad chopping (10-25 min) run in parallel     
 3 Pasta serving (25-30 min) follows immediately after boiling                  
 4 Salad mixing (30-35 min) completes the meal                                  

                             Constraints Verified:                              

✓ All steps scheduled exactly once                                              
✓ No resource conflicts (prep_area, stove, oven used without overlaps)          
✓ Precedence constraints respected (prep→boil→serve for pasta, chop→mix for     
salad)                                                                          
✓ Correct durations for all steps                                               
✓ Achieves expected optimal time of 35 minutes                                  

The solution has been saved to 055_recipe_planning_easy_task_code.py and        
produces the required JSON output format with both schedule and resource_usage  
fields.                                                                         

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

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

💬 Token Consumption:
  Input tokens:        218,115
  Output tokens:       7,421
  Total tokens:        225,536

⏱️  Execution time: 2m 17.8s
==================================================


=== STDERR ===
