codebase-inspection

Name: codebase-inspection
Availability: InStock
Author: Harries

Analyze repository code metrics and language composition with pygount.

Updated Apr 24, 2026

Search Tags:#codebase #code-analysis #loc #pygount #language-breakdown #repository-metrics

Ready to run

npx skills add https://github.com/Harries/hermes-agent --skill codebase-inspection-harries

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzing large codebases manually is time-consuming and error-prone; this skill provides automated LOC counts, language breakdown, and code-vs-comment ratios using pygount to give a clear snapshot of repo composition.

Core Features & Use Cases

LOC and file counts with language breakdown to understand code distribution.
Code-vs-comment ratio to assess documentation and readability.
Use Case: quickly assess a repository's size and language mix before planning refactors or audits.

Quick Start

Run pygount on the target repository to generate a language, code, and comment breakdown while excluding common dependency folders.

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 my repository?▼

To count lines of code and get a language breakdown, you can analyze repository code metrics using pygount. This generates reliable LOC counts, language distribution, and code-vs-comment ratios for your project.

What is a code-vs-comment ratio and when do I need it for a codebase analysis?▼

A code-vs-comment ratio measures documentation volume relative to executable lines in a codebase analysis. You need this metric to assess documentation readability and code composition during audit planning or refactor assessments.

Does pygount automatically exclude dependency folders like node_modules and dist during LOC analysis?▼

Yes, pygount analysis handles common dependency exclusions like node_modules and dist folders during LOC counting. This ensures the repository metrics reflect your actual project source code rather than external libraries.

How do I generate reproducible repository metrics suitable for dashboards and audits?▼

You generate reproducible repository metrics by running automated codebase analysis to produce consistent LOC counts and language breakdowns. This provides a reliable snapshot of repo composition suitable for dashboards and audits.

What is the best way to assess a repository's size and language mix before planning refactors?▼

The best way to assess repository size and language mix before refactors is to automate LOC counting and language distribution analysis. This provides a clear snapshot of code distribution to inform your planning decisions.

Why does manual codebase analysis take so long and how can I automate it?▼

Manual codebase analysis is time-consuming and error-prone because tracking files and languages by hand lacks consistency. You can automate it using pygount to generate accurate LOC counts and language breakdowns.