codebase-inspection

To count lines of code and get a language breakdown, this skill uses the pygount command-line tool to scan repository files, automatically excluding dependency folders like node_modules and venv. It generates structured summary tables or JSON output containing code metrics.

Calculating the code-to-comment ratio for a codebase is done by applying pygount to scan source files and measure comment density against executable lines. The skill outputs these ratios alongside file counts to support technical debt assessment.

Yes, you can exclude build and dependency directories when analyzing code metrics. The skill features built-in exclusions for common irrelevant folders like .git, node_modules, venv, and build directories to ensure accurate repository inspection.

Yes, the codebase analysis tool supports JSON output for automated reporting. It provides flexible output formats, allowing you to generate human-readable summary tables for quick insights or JSON data for programmatic use in scripts and dashboards.

Evaluating technical debt using code metrics is best achieved by scanning repository files to calculate lines of code and code-to-comment ratios. This skill filters analysis by specific programming languages to focus on relevant segments for segment-specific development reports.

Yes, you can filter codebase analysis results by specific programming languages. This targeted language filtering allows you to focus on relevant parts of a codebase, which is ideal for evaluating new open source projects or assessing technical debt.