codebase-inspection

Analyze repositories with pygount to calculate lines of code, language distribution, and code-to-comment ratios.

2|1|Updated Jul 14, 2026

Search Tags:#code analysis #codebase #metrics #repository #loc #pygount

Ready to run

npx skills add https://github.com/heysuhas/hermes_cli --skill codebase-inspection-heysuhas

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pygount.

What problem does it solve?

This Skill solves the difficulty of manually auditing large repositories to understand their scale, language distribution, and code-to-comment ratios.

Core Features & Use Cases

Language Breakdown: Automatically identifies the programming languages present and their respective file counts.
Metric Analysis: Calculates lines of code (LOC) and comment density to assess project maturity and documentation quality.
Use Case: Use this when onboarding to a new project to quickly determine which languages dominate the codebase and how much of the project is actual logic versus documentation.

Quick Start

Use the codebase-inspection skill to generate a summary report of the lines of code and language breakdown for the current directory.

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 distribution for a local repository?▼

You can calculate lines of code and language distribution for a local repository by analyzing its file-system structure using the pygount utility. This process automatically identifies programming languages present and aggregates lines of code for a comprehensive project summary.

What is the best way to analyze code-to-comment ratios during project onboarding?▼

Analyzing code-to-comment ratios during project onboarding involves scanning the codebase to measure comment density against actual logic lines. This assessment reveals project maturity and documentation quality, helping you quickly understand how much of the project is documented versus functional code.

Do I need pygount installed to analyze codebase metrics and file counts?▼

Yes, you need the pygount package installed in your local environment to analyze codebase metrics. This dependency is required to perform the necessary file-system traversal, identify programming languages, count files, and aggregate lines of code measurements.

Can I assess codebase health and language breakdown across various programming languages?▼

You can assess codebase health and language breakdown across various programming languages by running a repository analysis. The evaluation identifies the dominant languages present, calculates their respective file counts, and generates metrics to support technical audits.

How does a codebase size analysis tool handle technical audits of large repositories?▼

A codebase size analysis tool handles technical audits of large repositories by automating the traversal and aggregation of file metrics. Instead of manually auditing, it calculates lines of code, identifies language distribution, and reports comment density to evaluate overall project scale.