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codebase-inspection

Analyze codebases for lines of code and language distribution using pygount.

3|1|Updated Apr 19, 2024
Search Tags:#codebase#code metrics#repository analysis#loc#pygount#language breakdown
One-click install
npx skills add https://github.com/guccang/blogclaw --skill codebase-inspection-guccang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/guccang/blogclaw/tree/main/cmd/hermes-agent/vendor/hermes_runtime/skills/github/codebase-inspection
Command: npx skills add https://github.com/guccang/blogclaw --skill codebase-inspection-guccang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers quickly understand the size and composition of a codebase by analyzing lines of code, languages, file counts, and code-to-comment ratios.

Core Features & Use Cases

  • Repository Metrics Analysis: Generate code statistics and language breakdowns using pygount.
  • Code Composition Insights: Identify file counts, executable lines, documentation lines, and comment ratios for repositories.
  • Use Case: A developer reviewing a new project can use this Skill to measure repository complexity and understand its technology distribution before making changes.

Quick Start

Ask the Skill to analyze a repository and provide its lines of code and language composition summary.

Frequently Asked Questions about codebase-inspection

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

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

To measure lines of code and language breakdown for a repository, you can analyze the codebase using pygount to automatically generate source statistics. This process calculates executable lines, documentation lines, and file counts while excluding dependency directories.

What is codebase sizing and when do I need repository composition metrics?▼

Codebase sizing measures the total lines of code and file counts to gauge repository complexity. You need repository composition metrics when reviewing a new project to understand its technology distribution and code-to-comment ratios before making changes.

Does pygount exclude dependency directories when analyzing source code?▼

Yes, pygount excludes dependency directories when analyzing source code to produce reliable project statistics. This ensures the repository metrics accurately reflect your actual project composition rather than inflating counts with external libraries.

How do I get code-to-comment ratios and file counts for a software engineering review?▼

You can get code-to-comment ratios and file counts for a software engineering review by applying automated source analysis to the codebase. This generates code composition insights detailing executable lines versus documentation lines.

What is the best way to analyze a new codebase before making changes?▼

The best way to analyze a new codebase before making changes is to measure repository complexity and technology distribution. Generating language breakdowns and lines of code statistics helps you quickly understand project size and composition.

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