OpenSpace/gdpval_bench/skills/fallback-python-execution/SKILL.md

name	description
fallback-python-execution	Reliable Python execution workflow when execute_code_sandbox or shell_agent fail

Fallback Python Execution Pattern

When to Use

Use this pattern when:

• execute_code_sandbox returns unknown errors or fails repeatedly
• shell_agent cannot successfully execute Python code
• You need to create files (spreadsheets, documents, data files) via Python
• Direct delegated approaches prove unreliable in the current environment

Core Technique

Instead of delegating Python execution to agents, use this two-step inline approach:

1. Write Python code to a .py file using write_file
2. Execute the file using run_shell with python <script.py>

Step-by-Step Instructions

Step 1: Write Python Code to File

Use write_file to create a Python script with all necessary code inline:

write_file
path: /path/to/script.py
content: |
    import pandas as pd
    # Your complete Python code here
    df = pd.DataFrame({...})
    df.to_excel('output.xlsx', index=False)

Step 2: Execute via run_shell

Run the script directly:

run_shell
command: python /path/to/script.py

Step 3: Verify and Clean Up

• Check the output for success/errors
• Verify the expected files were created
• Optionally remove the temporary script if no longer needed

Why This Works

This approach is more reliable because:

• Avoids agent interpretation layers that can introduce errors
• Provides direct control over execution environment
• Gives clear error output for debugging
• Bypasses sandbox delegation issues

Example: Excel File Creation

# Step 1: Write the script
write_file:
  path: create_report.py
  content: |
    import pandas as pd
    from openpyxl import Workbook
    
    # Create data
    data = {'Column1': [1, 2, 3], 'Column2': ['A', 'B', 'C']}
    df = pd.DataFrame(data)
    
    # Save to Excel
    df.to_excel('report.xlsx', index=False)
    print('Excel file created successfully')

# Step 2: Execute
run_shell:
  command: python create_report.py

Tips

• Include error handling in your Python code for better debugging
• Use absolute paths when possible to avoid working directory issues
• Add print statements to track execution progress
• Keep scripts self-contained with all imports at the top
• For complex tasks, break into multiple scripts if needed

Troubleshooting

Issue	Solution
Module not found	Add pip install commands before python command
Permission errors	Check file paths are writable
Script not found	Use absolute path or cd to directory first
Output not created	Check for Python errors in run_shell output