Return code: 0
Duration: 103.8s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: print("=== Task 1: Problem Analysis ===")
  python_exec: import clingo
  python_exec: defining function on_model()
  python_exec: print("=== Task 6: JSON Output ===")
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

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

                                 Solution Found                                 

 • Selected tasks: [1, 2, 3, 5, 11]                                             
 • Total value: 470 (matching the expected optimal)                             
 • Diversity bonus: Achieved (all 3 categories represented)                     
 • Resource usage:                                                              
    • Compute: 140/150                                                          
    • Bandwidth: 120/120 (at capacity)                                          
    • Storage: 135/140                                                          
    • Specialists: 60/60 (at capacity)                                          

                           Key Implementation Details                           

 1 Conditional Specialist Cost: Properly modeled the rule where selecting any   
   development task (4-7) adds 5 specialist units per deployment task (8-11)    
 2 Diversity Bonus: Correctly tracks when all three categories (Research,       
   Development, Deployment) are represented and adds 100 to the total value     
 3 All Constraints Satisfied:                                                   
    • Resource limits respected                                                 
    • Prerequisite (Task 4 requires Task 0) - Task 4 not selected               
    • Mutual exclusion (Tasks 1 and 7) - Only Task 1 selected                   
    • Optimal value of 470 achieved                                             

The solution efficiently uses ASP's constraint-based approach to find the       
optimal allocation while respecting all complex dependencies and conditional    
costs.                                                                          

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

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

💬 Token Consumption:
  Input tokens:        112,198
  Output tokens:       5,899
  Total tokens:        118,097

⏱️  Execution time: 1m 42.9s
==================================================


=== STDERR ===
