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davpatel605-beep
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codebase-inspection

Analyze codebases with pygount to measure code metrics and language composition.

Updated Jun 25, 2026
Search Tags:#codebase#code metrics#repository analysis#loc#pygount#language breakdown
One-click install
npx skills add https://github.com/davpatel605-beep/hermusagent --skill codebase-inspection-davpatel605-beep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/davpatel605-beep/hermusagent/tree/main/backend/vendor/hermes/skills/github/codebase-inspection
Command: npx skills add https://github.com/davpatel605-beep/hermusagent --skill codebase-inspection-davpatel605-beep

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers quickly understand repository size, language composition, and code quality metrics without manually inspecting files or calculating statistics.

Core Features & Use Cases

  • Repository Metrics Analysis: Uses pygount to measure lines of code, file counts, language distribution, and code-to-comment ratios.
  • Codebase Composition Insights: Supports evaluating project size, technology mix, and overall repository structure.
  • Use Case: A developer can analyze a new GitHub repository to determine which languages dominate the codebase and estimate maintenance complexity.

Quick Start

Use the codebase-inspection skill to analyze this repository and provide a language breakdown with lines of code and 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 analyze a repository to get a language breakdown and lines of code metrics?▼

Repository size and composition can be measured by applying pygount to the codebase, which automatically generates file counts, language distribution statistics, and code-to-comment ratios while excluding dependency directories.

How do I measure the code-to-comment ratio in my codebase?▼

Codebase composition insights are provided by evaluating the code-versus-comment ratio using automated repository inspection, which helps estimate project maintenance complexity and technology mix.

Does the codebase analysis exclude dependency directories?▼

When analyzing a new GitHub repository, the Skill uses pygount to determine which languages dominate the codebase and excludes dependency directories to accurately estimate maintenance complexity.

What is the best way to get code metrics for a new GitHub repository?▼

Automated repository inspection with pygount distinguishes itself by generating file counts, language statistics, and code metrics directly, avoiding the need to manually inspect files or calculate statistics.

Do I need pygount to generate code metrics and file counts?▼

A known limitation of this approach is that it relies exclusively on pygount for source code size measurement, meaning it focuses on structural metrics rather than functional code quality or runtime behavior analysis.

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