techdebt

# Technical Debt Finder Identify and fix technical debt in the codebase. ## What to Look For ### Code Duplication - Functions with similar logic that could be consolidated - Copy-pasted code blocks - Repeated patterns that should be abstracted ### Dead Code - Unused imports - Unused functions or classes - Commented-out code blocks - Unreachable code paths ### Outdated Patterns - Deprecated API usage - Old-style string formatting (% or .format) vs f-strings - Type hints using `typing.List` instead of `list` - Missing type hints on public functions ### Code Smells - Functions longer than 50 lines - Too many parameters (more than 5) - Deep nesting (more than 3 levels) - Magic numbers without constants - Overly complex conditionals ### Missing Best Practices - Missing docstrings on public functions - Missing error handling - Hardcoded values that should be config - Missing tests for critical paths ## Workflow 1. **Scan the Codebase** - Look for patterns matching the issues above - Prioritize by impact and ease of fix 2. **Report Findings** - List issues by category - Include file paths and line numbers - Estimate severity (high/medium/low) 3. **Fix Issues** - Start with high-severity, easy fixes - Create atomic commits for each fix - Run tests after each change 4. **Verify** - Run linter: `ruff check .` - Run tests: `pytest` - Ensure no new issues introduced ## Arguments Optionally specify a directory or file to focus on. Usage: - `/techdebt` - Scan entire project - `/techdebt src/` - Scan specific directory - `/techdebt src/utils.py` - Scan specific file ## Output Provide a summary of: - Issues found (by category) - Issues fixed - Remaining items for future sessions