Verified:GitHub

codebase-inspection

Calculates lines of code, language composition, and code-to-comment ratios for repositories.

1|Updated Jun 25, 2026

Search Tags:#codebase analysis #code audit #pygount #repository metrics #code comment ratio #loc count #language metrics

Ready to run

npx skills add https://github.com/Signmanal/VIGIL --skill codebase-inspection-signmanal

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/Signmanal/VIGIL/tree/main/skills/github/codebase-inspection
Command: npx skills add https://github.com/Signmanal/VIGIL --skill codebase-inspection-signmanal

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually counting lines of code, mapping language composition, and calculating code-to-comment ratios for repositories is time-consuming and prone to error, especially for large codebases with nested dependency folders.

Core Features & Use Cases

Automated Code Metrics Generation: Quickly retrieve total lines of code, per-language file counts, and code/comment breakdowns for any repository.
Flexible Filtering Options: Target specific programming languages or exclude common dependency/build folders to get relevant, accurate results.
Use Case: A security analyst evaluating a new open source tool can use this skill to instantly assess the codebase's size, primary languages, and documentation coverage to gauge maintenance health and complexity.

Quick Start

Use the codebase-inspection skill to analyze the lines of code, language distribution, and comment ratios for the target repository at /path/to/your/repo.

Frequently Asked Questions about codebase-inspection

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calculate lines of code and language composition for a repository?▼

You can calculate lines of code and language composition by applying automated codebase analysis to the target repository. This process automatically extracts total lines of code, per-language file counts, and code-to-comment breakdowns to provide accurate metrics.

Why does my code audit show skewed lines of code metrics?▼

Code audit metrics often become skewed when dependency, build, and cache directories are included in the scan. Accurate metric generation requires automatically excluding these nested folders to prevent inflated line counts and inaccurate language distribution.

What is the best way to evaluate open source project health using code metrics?▼

Evaluating open source project health involves analyzing codebase size, primary languages, and documentation coverage. Calculating accurate code-to-comment ratios helps gauge maintenance complexity and documentation health for technical due diligence.

Can I filter specific programming languages when counting lines of code?▼

Yes, you can filter specific programming languages during repository analysis. Flexible filtering options allow you to target specific languages or exclude common dependency and build folders to retrieve relevant code metrics.

How do I track development team productivity using repository metrics?▼

Tracking development team productivity uses repository metrics like total lines of code and language composition. Automating codebase health assessment provides consistent data for evaluating output and documentation coverage.