codebase-inspection

Name: codebase-inspection
Availability: InStock
Author: entrovyx

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

3|1|Updated Jul 2, 2026

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

Ready to run

npx skills add https://github.com/EntroVyx/hermes-agent-offsec --skill codebase-inspection-entrovyx

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/EntroVyx/hermes-agent-offsec/tree/main/skills/github/codebase-inspection
Command: npx skills add https://github.com/EntroVyx/hermes-agent-offsec --skill codebase-inspection-entrovyx

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pygount.

What problem does it solve?

This skill addresses the difficulty of manually auditing large codebases to understand their size, language distribution, and documentation density.

Core Features & Use Cases

Language Breakdown: Automatically identify the programming languages present in a repository and their respective file counts.
Metric Calculation: Calculate lines of code (LOC) versus comment lines to determine code-to-documentation ratios.
Use Case: A developer needs to assess the complexity of a legacy project before starting a refactor, using this skill to generate a summary report of the codebase composition.

Quick Start

Use the codebase-inspection skill to analyze the current directory and provide a summary of the language breakdown and lines of code.

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 in a repository?▼

To calculate lines of code and language distribution in a repository, you can scan the directory to evaluate file counts and generate a codebase composition summary using the pygount utility.

Can I measure code-to-comment ratios for a legacy project before refactoring?▼

Yes, you can measure code-to-comment ratios for a legacy project by performing a file-by-file analysis that compares total lines of code against documentation density using pygount.

Do I need pygount installed to analyze codebase metrics?▼

Yes, you need the pygount Python package installed in your environment to perform accurate file-by-file analysis and calculate codebase metrics like lines of code and language breakdown.

What is the best way to audit a large codebase for documentation density?▼

Auditing a large codebase for documentation density is best handled by automatically identifying programming language file counts and calculating the ratio of comment lines to lines of code.

Does directory-level scanning support project size assessment for multiple languages?▼

Directory-level scanning supports project size assessment by automatically identifying the various programming languages present in a repository and calculating their respective file counts and metrics.

Why does codebase analysis require file-by-file scanning instead of sampling?▼

Codebase analysis requires file-by-file scanning to ensure accurate calculation of lines of code, language distribution, and code-to-comment ratios across the entire repository rather than estimating from samples.