Recover failed Python tasks by saving the code as a script and running it through the system shell. The workflow covers spreadsheet generation, data processing, output verification, and troubleshooting missing packages, paths, or permissions.
Recover from execute_code_sandbox failures by writing Python scripts to files and executing via run_shell
Review the instructions the agent will receive before you install.
Apply this pattern when execute_code_sandbox fails or times out, particularly for:
Extract or reconstruct the Python code that failed in execute_code_sandbox.
Use write_file to save the script with a .py extension:
write_file(
path="script_name.py",
content="""
import pandas as pd
# Your implementation here
"""
)
Run the script using the system Python interpreter:
run_shell(command="python3 script_name.py")
Confirm the results match expected outputs (files created, data processed correctly, etc.).
# Failed: execute_code_sandbox with pandas Excel generation
# Recovery - Step 1 & 2: Write script to file
write_file(
path="generate_pnl_report.py",
content="""
import pandas as pd
from openpyxl import Workbook
# Create sample data
data = {
'Category': ['Revenue', 'Expenses', 'Tax'],
'Amount': [10000, 3000, 500]
}
df = pd.DataFrame(data)
# Write to Excel
df.to_excel('pnl_report.xlsx', index=False)
print('Report generated: pnl_report.xlsx')
"""
)
# Step 3: Execute via shell
run_shell(command="python3 generate_pnl_report.py")
# Step 4: Verify file was created
run_shell(command="ls -la pnl_report.xlsx")
| Aspect | execute_code_sandbox | run_shell + write_file |
|---|---|---|
| Environment | Sandboxed, limited | Full system Python |
| File I/O | Restricted | Full access |
| Timeout | Strict limits | More flexible |
| Library Support | May be limited | System-installed packages |
| Result | Identical output | Identical output |
generate_report.py, process_data.pyIf run_shell also fails:
run_shell(command="python3 --version")run_shell(command="pip3 install pandas openpyxl")No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Prepare audio stems, the separate parts of a track, for mixing by matching their durations with silence, loops, or crossfades. Verify each generated and processed file before moving to the next production step.
Produce audio in separate stems, meaning individual instrument tracks, and verify each output as you go. Align track lengths with silence, loops, or crossfades, then apply effects, mix a master, and package the files.
Build serverless endpoints that relay external API requests for a dashboard while keeping API keys on the server. Includes Vercel examples for input checks, caching, cross-origin access, and consistent JSON responses.
Produce a master track and separate audio parts through a structured Python workflow. Apply filters and reverb, export WAV files with consistent settings, package them in a ZIP archive, and check that the deliverables meet the requested specifications.
Keep document creation moving when web searches and page retrieval repeatedly fail. The skill preserves the original task, uses the model’s existing knowledge, and clearly identifies information that could not be checked against external sources.
When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.