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codebase-inspection

Analyze repositories with pygount to compute lines of code and language distribution.

2|2|Updated Apr 16, 2026
Search Tags:#codebase#analysis#code-comments#loc#pygount#language-breakdown#repository-metrics
One-click install
npx skills add https://github.com/huidge/hermes-skills --skill codebase-inspection-huidge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/huidge/hermes-skills/tree/main/github/codebase-inspection
Command: npx skills add https://github.com/huidge/hermes-skills --skill codebase-inspection-huidge

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzing large codebases manually is time-consuming and error-prone. This Skill automates LOC counting, language breakdown, and code-vs-comment ratios to give a trustworthy snapshot of a repository.

Core Features & Use Cases

  • LOC & language breakdown: Produce precise counts by language and overall repository size.
  • Code-vs-comment ratio: Quantify code density to assess maintainability.
  • Use Case: Quickly compare multiple repositories to prioritize refactoring efforts and onboarding readiness.

Quick Start

Run the tool on your repository to generate a language and code-quality breakdown.

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 multi-language repository?▼

You can count lines of code and analyze language distribution by running pygount against your repository directories. The tool computes precise overall repository size and per-language counts, generating a structured summary suitable for reporting.

What is the code-to-comment ratio and how does it help assess codebase maintainability?▼

The code-to-comment ratio quantifies code density by comparing comment lines against executable lines using pygount. Assessing this ratio helps gauge maintainability and code quality, providing trustworthy metrics to compare multiple repositories for refactoring priorities.

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

Yes, you need pygount installed in your environment before analyzing repository metrics. The Skill requires this dependency to compute lines of code, language breakdowns, and code-vs-comment ratios, along with configuring sensible folder exclusions.

How do I exclude specific folders when calculating lines of code for accurate repository metrics?▼

You exclude specific folders by configuring sensible folder exclusions before running the codebase analysis. Excluding generated files, dependencies, and build directories ensures pygount computes accurate lines of code and language breakdown metrics for your source code.

Can I use this codebase analysis approach to compare multiple repositories for onboarding readiness?▼

Yes, you can use this codebase analysis to compare multiple repositories by generating language breakdowns and code-vs-comment ratios. These metrics help prioritize refactoring efforts and assess onboarding readiness by quantifying code density and size across teams.

What are the limitations of using pygount for codebase size estimation?▼

Limitations of using pygount for size estimation include relying on sensible folder exclusions to avoid skewed metrics from generated files. The tool provides quantitative language and comment ratios but does not assess structural code quality or architectural complexity within the repository.

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