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.
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Fallback workflow for reliable code execution when sandbox fails repeatedly
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Apply this pattern when you encounter repeated failures with execute_code_sandbox:
Track execution failures. After 2 consecutive failures with execute_code_sandbox, switch to the fallback approach.
Use write_file to save your Python script:
write_file(
path="/workspace/script_name.py",
content="# Your Python code here\nimport sys\n..."
)
Use run_shell to run the script:
run_shell(
command="python /workspace/script_name.py",
timeout=300
)
Parse stdout/stderr from run_shell output to verify success or diagnose issues.
# Instead of this (which may fail):
result = execute_code_sandbox(code="import pandas as pd\n...")
# Use this fallback pattern:
script_content = """
import pandas as pd
import sys
try:
# Your logic here
df = pd.DataFrame({'col': [1, 2, 3]})
print(df.to_csv())
sys.exit(0)
except Exception as e:
print(f"ERROR: {e}", file=sys.stderr)
sys.exit(1)
"""
# Write the script
write_file(path="/workspace/my_script.py", content=script_content)
# Execute via shell
result = run_shell(command="python /workspace/my_script.py", timeout=300)
sys.exit() codesrun_shell default is 30s, increase for heavy operationswrite_file is more reliable for file I/O operationsrun_shell gives you direct control over execution environment아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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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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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