codebase-inspection

You can count lines of code in a repository by running pygount to analyze source files, which calculates total lines and presents structured metrics for codebase sizing. It parses repository contents while excluding non-source directories to provide accurate LOC metrics.

The best way to analyze codebase language composition for an audit is to use pygount metrics, which provide a language breakdown with file counts, code lines, and comment lines. This generates a ready-to-consume report showing the language mosaic across your repository.

Yes, you can calculate code-vs-comment ratios across repositories of varying sizes using pygount. It identifies code lines and comment lines per language, delivering structured metrics that help developers assess documentation density during refactors or migrations.

To exclude non-source directories when analyzing repository metrics, pygount automatically filters out non-source files during the parsing process. This ensures the generated metrics report only counts actual source code, providing a clean language breakdown and accurate LOC data.

Yes, pygount works without external dependencies for codebase analysis. The skill requires no additional components to parse repository contents, quantify lines of code, and present structured metrics in a ready-to-consume format for language assessments and audits.