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code-exec-fallback-266cba

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

HKUDS/OpenSpace

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适用场景

Continuing Python work after two consecutive sandbox failures
Switching execution methods after persistent timeouts
Diagnosing script failures through error output
Setting longer timeouts for heavy processing

包含的文件（2）

SKILL.md2.5 KB
.skill_id39 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 Pattern

When to Use This Skill

Apply this pattern when you encounter repeated failures with execute_code_sandbox:

2+ consecutive failures with opaque or unknown errors
Timeout errors that persist across retry attempts
Environment-related errors that don't resolve with code fixes

The Fallback Workflow

Step 1: Detect Repeated Failures

Track execution failures. After 2 consecutive failures with execute_code_sandbox, switch to the fallback approach.

Step 2: Write Script to File

Use write_file to save your Python script:

write_file(
    path="/workspace/script_name.py",
    content="# Your Python code here\nimport sys\n..."
)

Step 3: Execute via Shell

Use run_shell to run the script:

run_shell(
    command="python /workspace/script_name.py",
    timeout=300
)

Step 4: Capture Output

Parse stdout/stderr from run_shell output to verify success or diagnose issues.

Complete Example

# 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)

Best Practices

Add error handling in your script - use try/except with sys.exit() codes
Set appropriate timeouts - run_shell default is 30s, increase for heavy operations
Clean up temporary files after execution if needed
Log the fallback trigger - document why you switched approaches
Verify Python availability - Most sandboxes have Python 3.x by default

Why This Works

write_file is more reliable for file I/O operations
run_shell gives you direct control over execution environment
Shell execution bypasses sandbox serialization issues
Better error visibility through stdout/stderr streams

When NOT to Use This Pattern

First-time execution failures (retry the sandbox first)
Simple one-liner code (sandbox is faster)
When sandbox errors are clearly code bugs (fix the code instead)
Security-sensitive operations requiring sandbox isolation

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中文简介正在准备中。当前显示英语简介。

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