prompt-decorators / rules

---
description: ENSURE careful modifications WHEN changing code TO prevent breakage
globs: **/*.py
---

# Careful Code Modification

<version>1.0.0</version>

## Context
- When modifying existing code in this codebase
- When fixing bugs or implementing new features
- When the codebase is fragile or has complex dependencies

## Requirements
- Make surgical, precise modifications targeting only the specific issue
- Implement proper validation procedures before and after changes
- Consider the scope and potential side effects of all changes
- Ensure changes are appropriate for the sensitivity level of the codebase
- Add thorough tests for any modifications made
- Document changes clearly with comments explaining the rationale

## Examples

<example>
# Good: Targeted fix with validation
def fix_calculation_error(data):
    # Validate input before processing
    if not isinstance(data, dict) or 'value' not in data:
        raise ValueError("Invalid data format")

    # Fix specific calculation issue only
    corrected_value = data['value'] * 1.05  # Apply 5% correction

    # Return with minimal changes to data structure
    result = data.copy()
    result['value'] = corrected_value
    return result
</example>

<example type="invalid">
# Bad: Overly broad changes without validation
def fix_calculation_error(data):
    # Completely restructuring data format unnecessarily
    new_data = {}
    for key, value in data.items():
        if key == 'value':
            new_data[key] = value * 1.05
        else:
            new_data[f"modified_{key}"] = process_data(value)

    return new_data  # Returns completely different structure
</example>