fallback-script-execution

# 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: 1. **Write script to file** using `write_file` 2. **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 1. **Add print statements** at key points to trace execution 2. **Check file paths** - use `run_shell` with `ls -la path/` to verify files exist 3. **Inspect errors** - run_shell output shows full Python stack traces 4. **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