codebase-inspection

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

9|Updated Jul 1, 2026

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

Ready to run

npx skills add https://github.com/Cyapstaye/Adame_ver.open --skill codebase-inspection-cyapstaye

💬 Prompt for ChatGPT / Claude Chat▼

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

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 size, language distribution, and code-to-comment ratios.

Core Features & Use Cases

Language Breakdown: Automatically identifies and counts files by programming language.
Metric Reporting: Calculates lines of code (LOC) and comment density to assess project complexity.
Use Case: Use this when you need to quickly audit a new repository to determine its primary technologies and overall scale before starting a code review or refactoring task.

Quick Start

Run the codebase inspection skill on the current directory to generate a summary report of all language statistics and line counts.

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 by running a codebase analysis tool that traverses the file system to count lines of code, identify programming languages, and measure documentation ratios automatically.

What is the best way to audit a new codebase before starting a refactoring task?▼

The best way to audit a new codebase for refactoring is to generate a summary report of language statistics and line counts, allowing you to determine primary technologies and overall scale quickly.

Does codebase analysis with pygount filter out dependency directories and build artifacts?▼

Yes, codebase analysis with pygount supports large-scale auditing by filtering out dependency directories and build artifacts, ensuring the reported metrics accurately reflect your actual source code.

Can I measure code-to-comment ratios and documentation density across a software project?▼

Yes, you can measure code-to-comment ratios and documentation density across a software project by calculating comment density metrics to assess overall project complexity and documentation coverage.

Do I need to install pygount locally to perform repository metrics analysis?▼

Yes, you need to install the pygount package in your local environment to perform file-system traversal and analysis, as the repository metrics calculation relies entirely on this utility.