Tech Debt Tracker

To identify technical debt automatically, the Skill scans your codebase using Python AST parsing and regex pattern matching. It detects code complexity, large function sizes, duplication, security vulnerabilities, and documentation gaps to provide actionable remediation insights.

The best way to prioritize technical debt for refactoring is by ranking items based on business impact and remediation effort. The Skill applies frameworks like Cost of Delay to help teams identify critical debt items for their next sprint planning session.

Yes, you can track technical debt trends over time using the Skill's Python scripts. It records analysis metrics chronologically, allowing you to visualize remediation progress and forecast the future health of your codebase.

Automated code analysis works effectively for finding security vulnerabilities and code duplication. The Skill leverages AST parsing to evaluate structural complexity and regex matching to detect patterns associated with duplicated code and security risks.

To scan your current directory for technical debt, you run the Skill's Python scripts with standard library modules like os, pathlib, and argparse. It analyzes the directory structure and outputs a prioritized list of code quality issues.