fallback-script-execution

Fallback Script Execution with write_file + run_shell

When to Use This Skill

Use this pattern when:

• shell_agent fails repeatedly with unclear error messages
• execute_code_sandbox consistently errors or times out
• You need better visibility into what's happening during execution
• Debugging inline code or delegated agents proves difficult

Core Pattern

Instead of delegating execution to an agent or running inline code, use this two-step approach:

1. Write script to file using write_file
2. Execute script using run_shell with python script.py

This provides:

• Clearer error messages (full stack traces visible in run_shell output)
• Easier debugging (script persists for inspection)
• Better control over execution environment
• Ability to modify and re-run without rewriting code

Step-by-Step Instructions

Step 1: Write the Script File

Use write_file to create a self-contained Python script:

write_file with:
  path: "path/to/script_name.py"
  content: |
    #!/usr/bin/env python3
    # Your complete script here
    # Include imports, logic, and error handling

Best Practices:

• Include descriptive comments
• Add try/except blocks for error handling
• Print intermediate results for debugging
• Use absolute or clear relative paths

Step 2: Execute the Script

Use run_shell to execute the script:

run_shell with:
  command: "python path/to/script_name.py"

Best Practices:

• Capture and examine full output
• If errors occur, the script file is still available for inspection
• You can re-run with modifications without starting over

Example: Data Processing Task

❌ Problematic Approach (shell_agent fails repeatedly)

shell_agent with:
  task: "Load Excel file, calculate correlations, save results"

Result: Agent struggles with path handling, unclear errors

✅ Recommended Approach (write_file + run_shell)

# Step 1: Write script
write_file with:
  path: "correlation_analysis.py"
  content: |
    import pandas as pd
    import sys
    
    try:
        # Load data
        df = pd.read_excel('data.xlsx', sheet_name='Returns')
        print(f"Loaded {len(df)} rows")
        
        # Calculate correlation
        corr = df.corr()
        print(f"Correlation matrix shape: {corr.shape}")
        
        # Save results
        with pd.ExcelWriter('output.xlsx') as writer:
            df.to_excel(writer, sheet_name='Returns')
            corr.to_excel(writer, sheet_name='Correlation')
        
        print("SUCCESS: output.xlsx created")
    except Exception as e:
        print(f"ERROR: {type(e).__name__}: {e}", file=sys.stderr)
        sys.exit(1)

# Step 2: Execute script
run_shell with:
  command: "python correlation_analysis.py"