Fallback Code Execution Workflow

Overview

This skill defines a robust workaround for executing code (specifically Python) when the primary

execute_code_sandbox

tool fails repeatedly with unknown or transient errors. Instead of continuing to retry the failing tool, the agent switches to a manual file-write and shell-execution pattern.

Trigger Conditions

Activate this workflow when:

1. execute_code_sandbox

   fails 2 or more times consecutively for the same logic.
2. Error messages are generic, unknown, or indicate environment issues rather than syntax errors.
3. The code logic itself is verified correct but the execution environment is unstable.

Procedure

Step 1: Write Script to File

Use the

write_file

tool to save the Python script to a specific path in the workspace.

• Path: Choose a descriptive name ending in

  .py

  (e.g.,

  scripts/generate_report.py

  ).
• Content: Ensure the script includes necessary error handling and print statements for debugging.
• Dependencies: If the script requires external libraries, ensure a

  requirements.txt

  is updated or installed via shell beforehand.

Example:

tool: write_file
path: workspace/scripts/process_data.py
content: |
  import sys
  # ... script logic ...
  print("Success")

Step 2: Execute via Shell

Use the

run_shell

tool to execute the script using the system Python interpreter.

• Command:

  python3 <path_to_script>

  or

  python <path_to_script>

  .
• Working Directory: Ensure the shell command runs from the workspace root or the directory containing the script.
• Capture Output: Store stdout and stderr for verification.

Example:

tool: run_shell
command: python3 scripts/process_data.py

Step 3: Verify Execution

1. Check Exit Code: Ensure the shell command returned exit code

   0

   .
2. Check Output: Verify expected files were created or expected stdout messages appeared.
3. Handle Errors: If the shell execution fails, inspect the stderr output. This often provides more detailed tracebacks than the sandbox tool.

Best Practices

• Absolute Paths: When writing scripts that access files, use absolute paths or resolve paths relative to

  __file__

  to avoid working directory issues.
• Permissions: Ensure the workspace directory allows file creation and execution.
• Cleanup: Optionally remove temporary scripts after successful execution if cleanliness is required.
• Logging: Add explicit

  print()

  statements in the Python script to log progress, as shell output capture is sometimes more reliable than sandbox return values.

Example Scenario

Problem:

execute_code_sandbox

times out while generating a PDF. Solution:

1. Write

   generate_pdf.py

   to

   workspace/scripts/

   .
2. Run

   python3 workspace/scripts/generate_pdf.py

   via

   run_shell

   .
3. Confirm

   output.pdf

   exists in the workspace.