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.
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Fallback pattern for executing Python code when execute_code_sandbox fails
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Use this pattern when execute_code_sandbox fails repeatedly (typically 2+ attempts) due to environment limitations, timeouts, dependency issues, or sandbox restrictions.
Instead of executing code directly in the sandbox, write the Python script to a file and execute it via shell:
write_filerun_shell with python3 script.pyUse write_file to save your Python code:
- Path: Choose a descriptive name (e.g., "process_data.py", "analyze.py")
- Content: Your complete Python script with all imports and logic
Use run_shell to execute:
- Command: "python3 <script_name>.py"
- Timeout: Set appropriately for your task (default 30s, increase if needed)
- Capture stdout/stderr from run_shell
- Parse results as needed
- Optionally delete the script file after execution
# Instead of this (which may fail):
execute_code_sandbox(code="import pandas as pd; df = pd.read_csv('data.csv')...")
# Do this:
write_file(path="analyze.py", content="""
import pandas as pd
import json
df = pd.read_csv('data.csv')
result = df.groupby('category').sum()
print(json.dumps(result.to_dict()))
""")
run_shell(command="python3 analyze.py", timeout=60)
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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.
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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.
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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.
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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.
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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.
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Recover from repeated sandbox execution failures by saving Python code to a script and running it through run_shell. Check output, error messages, and exit codes to confirm results or diagnose remaining execution problems.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.