analysis-code

To analyze code quality and identify technical debt, you can trigger a structured analysis on your Python project to generate quantitative metrics and actionable recommendations. This applies a Plan-Execute-Report workflow across modules to guide refactoring decisions.

Static analysis metrics for code quality include complexity, lines of code, documentation, and test coverage proxies. These quantitative indicators benchmark project health and highlight modules requiring refactoring to reduce technical debt.

Yes, you can apply this code analysis approach across multiple modules in a software project. It systematically evaluates project health holistically to produce structured artifacts and actionable improvement suggestions for your top modules.

The best way to structure a codebase analysis workflow is using a Plan-Execute-Report approach. This systematically guides multi-step analysis from planning to reporting, producing baseline reports and actionable recommendations to drive refactoring decisions.

No, this technical debt analysis does not require external dependencies or tools. You can enable the Skill directly in your editor and trigger an analysis on a local Python project to obtain a baseline report.

You should generate quantitative code metrics before refactoring to establish a baseline for project health. These metrics evaluate complexity and test coverage proxies, ensuring your refactoring decisions are driven by measurable data rather than guesswork.