codebase-inspection

To count lines of code and analyze language distribution, scan the target directory to generate LOC counts, per-language percentages, and code-vs-comment ratios. This uses pygount to compute metrics while excluding common artifacts, returning structured outputs for dashboards or repo audits.

A code-vs-comment ratio measures the balance between executable lines and documentation within a codebase. You need to calculate it during code quality reviews or onboarding to assess documentation effort and guide refactoring priorities across single repositories or multi-repo portfolios.

Yes, you can use pygount to analyze code composition across multi-repo portfolios. The tool computes per-language counts and percentages, supporting CLI-based usage to deliver reproducible reports and structured outputs suitable for dashboard integration.

Yes, the codebase inspection process excludes common artifacts when computing total lines of code and comments. It relies on pygount to measure actual source code composition, ensuring accurate language breakdowns and code-vs-comment ratios for data-driven repo management.

The best way to generate reproducible LOC reports is using CLI-based analysis to scan the target directory. This delivers structured outputs with lines of code, language distribution, and code-vs-comment metrics, enabling data-driven code quality assessments for onboarding new projects.