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codebase-inspection

Quantify repository size and language composition using pygount.

Updated Jun 17, 2026
Search Tags:#codebase#metrics#inspect#languages#loc#pygount
One-click install
npx skills add https://github.com/anilcan-kara/nozich-agent --skill codebase-inspection-anilcan-kara
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/anilcan-kara/nozich-agent/tree/main/skills/github/codebase-inspection
Command: npx skills add https://github.com/anilcan-kara/nozich-agent --skill codebase-inspection-anilcan-kara

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzing a codebase manually is time-consuming. This Skill automates the collection of key metrics (LOC, language distribution, file counts, and code-vs-comment ratios) so you can quickly understand a repository's composition.

Core Features & Use Cases

  • Language breakdown: report per-language counts and lines to understand where the code lives.
  • Size & composition: total LOC, code vs comments, and file counts to gauge complexity.
  • Use Case: before refactoring, or during audit, to identify hotspots and dependencies in a repository; ideal for code reviews and onboarding.

Quick Start

Run the inspection on a repository path to generate LOC, language breakdown, and code-vs-comment ratios using pygount.

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 breakdown for a repository?▼

You can calculate lines of code and language breakdown by running an automated inspection on a repository path. This process uses pygount to generate total LOC, per-language counts, and code-vs-comment ratios for any codebase.

What is the best way to automate codebase metrics collection before a refactor?▼

Automating codebase metrics collection involves running a tool like pygount against your repository path. This yields file counts, language distribution, and code-to-comment ratios, helping you identify hotspots and dependencies before refactoring.

Does pygount support analyzing codebases written in any programming language?▼

Yes, pygount supports analyzing codebases of any programming language. It quantifies repository size and composition by scanning the repository contents to obtain LOC and language-specific metrics.

Can I get code-vs-comment ratios and file counts for an audit?▼

Yes, you can get code-vs-comment ratios and file counts for an audit by applying pygount metrics to the repository. This quantifies repository composition and complexity, making it ideal for code reviews and onboarding.

How do code inspection metrics help with codebase onboarding?▼

Code inspection metrics help with onboarding by providing a quick overview of repository composition. New developers can understand where the code lives through language breakdowns and gauge complexity using total LOC and file counts.

Do I need to install pygount to inspect my repository's LOC?▼

Yes, you need to install pygount and standard Python tooling to inspect your repository's LOC. These dependencies are required to analyze the repository contents and generate the codebase metrics.

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