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fallback-python-execution

Reliable Python execution workflow when execute_code_sandbox or shell_agent fail

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HKUDS/OpenSpace
Letzte Quellaktivität
17. Juli 2026 um 03:43
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Englisch
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926

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
fallback-python-execution
description
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 ```yaml # 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 |
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