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codebase-inspection

Analyze codebases for lines of code, language composition, and code-vs-comment ratios using pygount.

6|Updated Apr 26, 2026
Search Tags:#codebase#static-analysis#loc#pygount#language-breakdown#repository-metrics
One-click install
npx skills add https://github.com/Strategic-Automation/arachne --skill codebase-inspection-strategic-automation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/Strategic-Automation/arachne/tree/main/src/arachne/skills/default/github/codebase-inspection
Command: npx skills add https://github.com/Strategic-Automation/arachne --skill codebase-inspection-strategic-automation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Codebase health and size are hard to gauge from files alone; this skill provides quick, reproducible measurements of lines of code, language distribution, and code-to-comment ratios using pygount.

Core Features & Use Cases

  • Line-of-code counting: total LOC broken down by language with file counts.
  • Language breakdown: distribution across programming languages and file types.
  • Code-vs-comment ratio: computes actionable metrics to assess maintainability.
  • Use Case: When starting a new project or auditing an existing repo, generate a concise metrics snapshot for planning and reporting.

Quick Start

Run this skill on your repository to generate a LOC and language breakdown report.

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?▼

You can generate a language breakdown and lines of code count by running this skill on your repository, which uses pygount to analyze code composition and produce JSON or summary outputs for reporting.

What is the code-to-comment ratio and how does it indicate repository health?▼

The code-to-comment ratio measures the proportion of comments relative to executable lines of code. This skill uses pygount to compute these metrics, providing an actionable indicator of codebase maintainability and documentation health.

Does pygount support language metrics for both Python and JavaScript codebases?▼

Yes, pygount supports language metrics for Python, JavaScript, and other programming ecosystems. The skill analyzes codebases to provide language distribution and LOC totals across multiple file types and programming languages.

Can I exclude specific folders when calculating lines of code for a large project?▼

Yes, you can exclude specific folders when calculating lines of code. The skill enforces proper CLI usage with folder-exclusion patterns, allowing accurate repository metrics generation for software projects of any size.

What's the best way to generate a reproducible LOC and code metrics snapshot?▼

The best way to generate a reproducible LOC snapshot is using this skill's pygount integration, which provides a repeatable analytics workflow supporting JSON and summary outputs for consistent codebase health reporting.

When do I need static analysis for lines of code and repository metrics?▼

You need static analysis for repository metrics when starting a new project or auditing an existing repo. This skill provides quick, reproducible measurements of lines of code and language composition for planning and reporting.

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