
Code Exec Fallback
- 2 installs
- 7.3k repo stars
- Updated July 27, 2026
- hkuds/openspace
Falls back to writing a Python script to file and running it via shell when execute_code_sandbox fails repeatedly.
About
Provides a fallback pattern for running Python code when execute_code_sandbox fails repeatedly: write the script to a file and execute it via shell instead. A developer uses it when the code sandbox hits environment limits, timeouts, or dependency issues.
- Triggers after 2+ execute_code_sandbox failures
- Writes script to file and runs via shell (run_shell python3)
Code Exec Fallback by the numbers
- 2 all-time installs (skills.sh)
- Ranked #464 of 597 Debugging skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
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| Installs | 2 |
|---|---|
| repo stars | ★ 7.3k |
| Last updated | July 27, 2026 |
| Repository | hkuds/openspace ↗ |
What it does
Falls back to writing a Python script to file and running it via shell when execute_code_sandbox fails repeatedly.
Files
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 logicStep 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 executionExample
# 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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