Fallback Code Execution

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 or python .
• 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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: HKUDS
• Source: HKUDS/OpenSpace
• License: MIT

Install and usage instructions live in the source repository linked above.