codebase-inspection

Analyze repository codebases for lines of code, language breakdown, and code-versus-comment ratios.

Updated Apr 2, 2026
One-click install
npx skills add https://github.com/JKhyro/HERMES-AGENT --skill codebase-inspection-jkhyro
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/JKhyro/HERMES-AGENT/tree/main/skills/github/codebase-inspection
Command: npx skills add https://github.com/JKhyro/HERMES-AGENT --skill codebase-inspection-jkhyro

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill removes the guesswork from understanding a repository’s size and composition by turning a codebase into clear metrics for lines of code, language distribution, and comment balance.

Core Features & Use Cases

  • Repository Metrics: Count code, comments, and file totals to estimate scope and maintenance load.
  • Language Breakdown: Identify which languages dominate a project and how much each contributes.
  • Use Case: A developer can quickly assess whether a monorepo is Python-heavy, whether generated files are inflating counts, or whether a code review sprint needs to focus on a specific subsystem.

Quick Start

Ask for a pygount-based inspection of the target repository and include any folders that should be excluded from the scan.

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 get a language breakdown for a repository?

You can measure lines of code and language breakdown by running a pygount-based codebase inspection that scans source trees to report code totals, language distribution, and comment-to-code ratios.

How do I exclude dependency and build directories when analyzing a codebase?

To exclude dependency and build directories when analyzing a codebase, provide specific folder exclusion controls during the pygount scan to ensure it skips generated files and traverses only your source tree.

Does pygount work for mixed-language source trees in a monorepo?

Yes, pygount works for mixed-language source trees in a monorepo by analyzing the repository to identify which languages dominate the project and exactly how much each language contributes to the overall codebase size.

What is the best way to check code-versus-comment ratios across a large project?

The best way to check code-versus-comment ratios across a large project is to perform a repository metrics analysis that calculates the comment balance, helping you estimate scope and maintenance load for a code review sprint.

Can I measure if generated files are inflating my repository size metrics?

You can measure if generated files are inflating your repository size metrics by running a codebase inspection and applying folder exclusion controls to separate actual source code from build artifacts and dependency directories.

What are the limitations of using pygount for a repository size audit?

A limitation of using pygount for a repository size audit is that it requires manual folder exclusion controls to avoid traversing dependency or build directories, otherwise generated files will inflate the lines of code and language breakdown metrics.