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code-exec-fallback

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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适用场景

  • Retry Python work through the shell after repeated sandbox failures
  • Run longer tasks with an adjusted timeout
  • Execute Python work that needs multiple files or system utilities
  • Return analysis results as JSON for further processing

包含的文件(2)

  • SKILL.md2.3 KB
  • .skill_id32 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Code Execution Fallback

When to Use

Use this pattern when execute_code_sandbox fails repeatedly (typically 2+ attempts) due to environment limitations, timeouts, dependency issues, or sandbox restrictions.

The Pattern

Instead of executing code directly in the sandbox, write the Python script to a file and execute it via shell:

  1. Write the script using write_file
  2. Execute via shell using run_shell with python3 script.py
  3. Clean up (optional) remove the temporary file

Step-by-Step Instructions

Step 1: Write the Python Script

Use 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

Step 2: Execute via Shell

Use run_shell to execute:
- Command: "python3 <script_name>.py"
- Timeout: Set appropriately for your task (default 30s, increase if needed)

Step 3: Handle Output

- Capture stdout/stderr from run_shell
- Parse results as needed
- Optionally delete the script file after execution

Example

# 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)

Tips for Success

  1. Include all imports in the script file - the shell environment may differ from the sandbox
  2. Use absolute paths or ensure working directory is correct
  3. Add error handling to your script for better debugging
  4. Increase timeout for long-running operations (default is 30s)
  5. Print structured output (JSON) if you need to parse results
  6. Clean up temporary files after successful execution to avoid clutter

When This Helps

  • Sandbox has missing dependencies
  • Code execution times out in sandbox but would work in shell
  • File I/O operations are restricted in sandbox
  • Need to run external commands or system utilities
  • Complex multi-file projects that need proper file structure

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