Code Execution Fallback & Workspace Anchoring

This skill provides a robust pattern for executing code when the primary method fails, combined with proper workspace path management to prevent file location errors.

Core Techniques

1. Workspace Path Anchoring

Always establish and verify your working directory at the start of any task:

# At the beginning of any code execution
import os
workspace_path = os.getcwd()
print(f"Working directory: {workspace_path}")

# In shell scripts
pwd
echo "Current directory: $(pwd)"

Why: Prevents files from being written to unexpected locations when agents switch between tools.

2. Execution Fallback Ladder

When execute_code_sandbox fails, follow this escalation pattern:

Level 1: Retry with Simpler Code

• Simplify the code structure
• Remove complex dependencies
• Add explicit error handling

Level 2: Use run_shell with Heredoc

When sandbox execution repeatedly fails, switch to shell execution:

python3 << 'EOF'
import os
import pandas as pd

# Your code here
data = {"col1": [1, 2, 3], "col2": ["a", "b", "c"]}
df = pd.DataFrame(data)
df.to_csv("output.csv", index=False)
print("File written successfully")
EOF

Key points:

• Use << 'EOF' (quoted) to prevent variable expansion
• Include all imports and dependencies inline
• Add explicit success/failure messages

Level 3: Delegate to shell_agent

For complex multi-step tasks with error recovery needs:

Task: Create a data processing pipeline that reads CSV, transforms data, and outputs Excel
Requirements:
- Handle missing values
- Apply transformations
- Write to ./output/ directory
- Retry on transient errors

3. Explicit Path Management

Always use absolute or explicitly relative paths:

# BAD - relies on implicit working directory
df.to_csv("output/data.csv")