codebase-inspection

Name: codebase-inspection
Availability: InStock
Author: rickyananda

Count repository lines of code and language composition using pygount.

1|Updated May 18, 2026

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

Ready to run

npx skills add https://github.com/rickyananda/hermes-skills --skill codebase-inspection-rickyananda

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill removes the guesswork from understanding how large and complex a repository is by turning raw source files into clear LOC, language, and comment metrics.

Core Features & Use Cases

Repository Size Analysis: Measure total lines of code and compare source composition across a project.
Language Breakdown: Identify which languages dominate a codebase and how much each contributes.
Code vs. Comment Ratios: Compare implementation density with documentation-heavy files.
Use Case: A developer can inspect a monorepo before planning refactors, dependency cleanup, or maintenance estimates.

Quick Start

Ask the skill to analyze the target repository with pygount and summarize its LOC, language distribution, file counts, and code-versus-comment ratios.

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 language breakdown in a repository?▼

To count lines of code and language breakdown, use pygount to summarize total code lines, file counts, comment lines, and folder exclusions. This provides accurate source composition metrics for monorepos and software audits.

What is a codebase inspection used for in software engineering?▼

Codebase inspection identifies which languages dominate a project and measures code-versus-comment ratios. It removes guesswork by turning raw source files into clear LOC, language, and comment metrics for planning refactors or maintenance estimates.

Can I analyze a monorepo to compare code and comment ratios across source trees?▼

Yes, you can analyze a monorepo to compare implementation density with documentation-heavy files. The inspection applies pygount across source trees to summarize file counts and special file classes accurately for maintenance planning.

Do I need pygount to measure repository size and language composition?▼

Yes, pygount is required to measure repository size and language composition accurately. It processes source trees to count total lines of code, comment lines, and special file classes while applying folder exclusions for precise metrics.

What is the best way to audit language distribution before planning a refactor?▼

The best way to audit language distribution before a refactor is inspecting the codebase with pygount to summarize file counts, code lines, and comment breakdowns, revealing exactly how much each programming language contributes.