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lucasfth
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codebase-inspection

Analyze Git repositories to count LOC, language breakdown, and code-vs-comment ratios.

1|Updated Apr 30, 2025
Search Tags:#codebase#metrics#code-analysis#loc#pygount#language-breakdown#repo-metrics
One-click install
npx skills add https://github.com/lucasfth/config --skill codebase-inspection-lucasfth
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/lucasfth/config/tree/main/.hermes/skills/github/codebase-inspection
Command: npx skills add https://github.com/lucasfth/config --skill codebase-inspection-lucasfth

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze codebases to count lines of code, identify language distribution, and compute code-vs-comment ratios for quick assessments.

Core Features & Use Cases

  • LOC counting and language breakdown across a repository
  • Code-vs-comment ratio analysis to gauge maintainability
  • Use Case: Evaluate a project to understand language mix and overall size for planning and onboarding.

Quick Start

Run the pygount-based analysis on your repository to generate a comprehensive codebase metrics report.

Frequently Asked Questions about codebase-inspection

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

FAQPage Schema
How do I count lines of code and analyze language distribution in a Git repository?▼

You can count lines of code and analyze language distribution by running a pygount-based analysis on your Git repository or local clone. It generates a comprehensive metrics report detailing the overall size and language mix across the codebase.

How do I calculate code-to-comment ratios for a project health check?▼

To calculate code-to-comment ratios for a project health check, you can use a codebase analysis tool that computes these metrics automatically. This helps gauge maintainability by comparing functional code against comments across various programming languages.

Does pygount work across multiple programming languages for repository metrics?▼

Yes, pygount works across multiple programming languages to provide repository metrics. It handles common languages for LOC counting and language breakdown, making it suitable for quick repo health checks and language distribution audits.

Can I exclude dependency directories when counting lines of code?▼

Yes, you can exclude large dependency directories when counting lines of code. The analysis supports configurable folders-to-skip, ensuring that generated files and third-party dependencies do not skew your codebase size estimates.

Do I need a Python environment to analyze codebase metrics and LOC?▼

Yes, you need a Python environment to analyze codebase metrics and LOC. The analysis requires pygount to be installed in your Python environment to perform the repository size estimates and language breakdowns.

What is the best way to audit codebase size and language mix for onboarding?▼

The best way to audit codebase size and language mix for onboarding is to generate a comprehensive codebase metrics report. By counting LOC and identifying language distribution, you can evaluate a project to understand its overall size and plan effectively.

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