| name | fallback-script-execution |
| description | Two-step script execution workflow for debugging when shell_agent and execute_code_sandbox consistently fail |
Fallback Script Execution with write_file + run_shell
When to Use This Skill
Use this pattern when:
shell_agent fails repeatedly with unclear error messages
execute_code_sandbox consistently errors or times out
- You need better visibility into what's happening during execution
- Debugging inline code or delegated agents proves difficult
Core Pattern
Instead of delegating execution to an agent or running inline code, use this two-step approach:
- Write script to file using
write_file
- Execute script using
run_shell with python script.py
This provides:
- Clearer error messages (full stack traces visible in run_shell output)
- Easier debugging (script persists for inspection)
- Better control over execution environment
- Ability to modify and re-run without rewriting code
Step-by-Step Instructions
Step 1: Write the Script File
Use write_file to create a self-contained Python script:
write_file with:
path: "path/to/script_name.py"
content: |
#!/usr/bin/env python3
# Your complete script here
# Include imports, logic, and error handling
Best Practices:
- Include descriptive comments
- Add try/except blocks for error handling
- Print intermediate results for debugging
- Use absolute or clear relative paths
Step 2: Execute the Script
Use run_shell to execute the script:
run_shell with:
command: "python path/to/script_name.py"
Best Practices:
- Capture and examine full output
- If errors occur, the script file is still available for inspection
- You can re-run with modifications without starting over
Example: Data Processing Task
❌ Problematic Approach (shell_agent fails repeatedly)
shell_agent with:
task: "Load Excel file, calculate correlations, save results"
Result: Agent struggles with path handling, unclear errors
✅ Recommended Approach (write_file + run_shell)
# Step 1: Write script
write_file with:
path: "correlation_analysis.py"
content: |
import pandas as pd
import sys
try:
# Load data
df = pd.read_excel('data.xlsx', sheet_name='Returns')
print(f"Loaded {len(df)} rows")
# Calculate correlation
corr = df.corr()
print(f"Correlation matrix shape: {corr.shape}")
# Save results
with pd.ExcelWriter('output.xlsx') as writer:
df.to_excel(writer, sheet_name='Returns')
corr.to_excel(writer, sheet_name='Correlation')
print("SUCCESS: output.xlsx created")
except Exception as e:
print(f"ERROR: {type(e).__name__}: {e}", file=sys.stderr)
sys.exit(1)
# Step 2: Execute script
run_shell with:
command: "python correlation_analysis.py"
Debugging Tips
- Add print statements at key points to trace execution
- Check file paths - use
run_shell with ls -la path/ to verify files exist
- Inspect errors - run_shell output shows full Python stack traces
- Modify and re-run - edit the script file and execute again without rewriting
When to Escalate
If this pattern also fails:
- Verify Python is available:
run_shell with which python or python --version
- Check file permissions:
run_shell with ls -la script.py
- Try explicit Python path:
run_shell with /usr/bin/python script.py
- Consider task complexity - may need to break into smaller scripts