codebase-inspection

Analyze codebases for lines of code, language usage, and code-to-comment ratios with pygount.

Updated May 25, 2026

Search Tags:#code analysis #metrics #repository #pygount #lines of code

Ready to run

npx skills add https://github.com/zaiyemeiyou404/Hermes --skill codebase-inspection-zaiyemeiyou404

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/zaiyemeiyou404/Hermes/tree/main/backup/skills/github/codebase-inspection
Command: npx skills add https://github.com/zaiyemeiyou404/Hermes --skill codebase-inspection-zaiyemeiyou404

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pygount, and includes scripts (resource) components.

What problem does it solve?

This Skill provides a detailed analysis of codebases, helping users understand the size, composition, and health of their repositories.

Core Features & Use Cases

LOC Count: Obtain the total number of lines of code in a repository.
Language Breakdown: Get a detailed analysis of the programming languages used.
File Composition: Understand the file structure and the types of files present in the repository.
Code vs. Comment Ratio: Assess the ratio of code to comments, indicating code density.
Use Case: When a developer wants to quickly assess the size and complexity of a new codebase they are about to work on.

Quick Start

Run 'pygount --format=summary /path/to/repo' to get a language breakdown and file count for the codebase at /path/to/repo.

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 usage in a repository?▼

To count lines of code and analyze language usage in a repository, use the pygount library to scan your codebase and generate a summary of total lines, language distribution, and file composition metrics.

What is the code-to-comment ratio and how does it indicate codebase health?▼

The code-to-comment ratio measures code density by comparing the volume of code lines to comment lines. Assessing this ratio helps indicate codebase health and understand documentation coverage within a repository.

Can I use pygount to assess the size and complexity of a new codebase?▼

Yes, you can use pygount to assess a new codebase's size and complexity. It provides detailed statistics on total lines of code, language breakdown, and file composition to help you understand repository structure quickly.

Do I need to install pygount before analyzing repository metrics?▼

Yes, you must install and configure the pygount library before using this Skill. It requires pygount as a dependency to execute codebase analysis and calculate metrics like lines of code and code-to-comment ratios.

What is the best way to get a language breakdown for my codebase?▼

The best way to get a language breakdown is running the pygount command with the summary format against your repository path. This analyzes the codebase to provide detailed statistics on programming languages used and file counts.