codebase-inspection

Name: codebase-inspection
Availability: InStock
Author: zangjeicy

Analyze software repositories with pygount for code metrics and language distribution.

Updated Jul 13, 2026

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

Ready to run

npx skills add https://github.com/zangjeicy/Hermes --skill codebase-inspection-zangjeicy

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pygount.

What problem does it solve?

This skill solves the challenge of quickly understanding the scale, language distribution, and complexity of a software project without manual file counting.

Core Features & Use Cases

Language Breakdown: Automatically identifies programming languages and their respective file counts.
Code Metrics: Calculates lines of code (LOC) versus comment lines to determine documentation density.
Use Case: Use this when onboarding to a new repository to quickly assess its size, identify the primary languages used, and verify the ratio of code to comments.

Quick Start

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

Frequently Asked Questions about codebase-inspection

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze lines of code and language distribution in a repository?▼

This skill analyzes lines of code and language distribution in a repository by performing file-by-file scanning to calculate total LOC, identify programming languages, and measure code-to-comment ratios.

What is the best way to calculate code-to-comment ratios during project onboarding?▼

Calculating code-to-comment ratios during project onboarding is done by scanning the repository to compare lines of code against comment lines. This provides a rapid assessment of documentation density and project scale without manual counting.

Do I need pygount installed to scan codebase metrics?▼

Yes, you need the pygount library installed in your environment to scan codebase metrics. The skill relies on this dependency to perform accurate file-by-file scanning and aggregate lines of code statistics.

Can I automatically identify programming languages and their file counts in a directory?▼

You can automatically identify programming languages and their respective file counts in a directory by running the codebase inspection skill. It scans the current directory to generate a summary report of all language statistics and code metrics.

What are the limitations of using pygount for codebase analysis?▼

A limitation of using pygount for codebase analysis is that it primarily focuses on quantitative metrics like lines of code and comment ratios, rather than providing qualitative insights into code complexity, structural dependencies, or architectural patterns.