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DaddyElonMusk69
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codebase-inspection

Generate lines-of-code statistics, language breakdowns, and code-vs-comment ratios for a repository.

Updated Apr 12, 2026
Search Tags:#codebase#metrics#repository-analysis#loc#pygount#language-breakdown
One-click install
npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill codebase-inspection-daddyelonmusk69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/DaddyElonMusk69/motis-agent/tree/main/skills/github/codebase-inspection
Command: npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill codebase-inspection-daddyelonmusk69

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It eliminates the guesswork around repository size and composition by providing clear lines‑of‑code counts, language distribution, and code‑vs‑comment ratios.

Core Features & Use Cases

  • LOC Counting: Quickly determine the total lines of code in any project.
  • Language Breakdown: See how many files and lines belong to each programming language.
  • Code‑vs‑Comment Ratios: Assess documentation quality and code density.
  • Use Case: When comparing two codebases for technical debt or evaluating the impact of adding a new language to a project, this Skill delivers the needed metrics instantly.

Quick Start

Ask the skill to generate a full summary of lines of code and language distribution for the target repository.

Frequently Asked Questions about codebase-inspection

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

FAQPage Schema
How do I get lines of code and language breakdown statistics for a repository?▼

Lines of code statistics and language breakdowns are generated by analyzing the repository to provide total LOC, file counts per language, and code-vs-comment ratios. This instantly reveals repository size, composition, and documentation density.

What is the best way to compare codebase composition across projects?▼

Comparing codebase composition across projects is best done by generating language distribution and code-vs-comment ratios for each repository. This delivers consistent metrics to evaluate technical debt or assess the impact of adding new programming languages.

Do I need pygount installed to analyze code-to-comment ratios?▼

Yes, pygount must be installed and accessible from the command line to analyze code-to-comment ratios. The tool executes summary, JSON, or filtered language analyses via pygount to calculate documentation density and code metrics.

How do I extract code metrics and language distribution into JSON?▼

Extracting code metrics and language distribution into JSON involves running a command-line summary analysis on the target repository. This generates structured output containing lines-of-code counts and file statistics for each programming language.

Can I filter language breakdowns to specific programming languages in a repository?▼

Yes, you can filter language breakdowns to specific programming languages by executing filtered language analyses via the command line. This isolates file counts and lines of code for individual languages within the repository.

When do I need lines of code statistics for repository analysis?▼

Lines of code statistics are needed when assessing repository size, evaluating technical debt, or comparing codebases before adding a new programming language. They eliminate guesswork by providing clear counts and code density metrics.

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