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ruiyangruiyi
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codebase-inspection

Analyze repository code size and language composition with pygount.

Updated May 26, 2026
Search Tags:#codebase inspection#pygount#language breakdown#lines of code#repository metrics#code vs comments
One-click install
npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill codebase-inspection-ruiyangruiyi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/ruiyangruiyi/hermes-agent/tree/main/skills/github/codebase-inspection
Command: npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill codebase-inspection-ruiyangruiyi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you quantify how big a codebase is and what it is made of by producing reliable lines-of-code and language breakdown metrics.

Core Features & Use Cases

  • Repository LOC & language breakdown: Counts code lines, comment/doc lines, and file counts by detected language.
  • Code-vs-comment ratios: Helps you understand documentation density and code/comment balance.
  • Practical exclusions for speed: Recommends skipping dependency/build folders to avoid long scans or hangs.

Example use case: you need to estimate engineering effort for onboarding a new repository and want a quick, defensible snapshot of language mix and approximate LOC.

Quick Start

Ask the agent to inspect a repository’s lines of code and language breakdown using pygount while skipping common dependency/build directories.

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 a repository?▼

To measure lines of code and language breakdown, you use pygount to analyze the repository source tree. It counts code, comment, and documentation lines by detected language for a quick composition snapshot.

What is a code-vs-comment ratio and why does it matter for technical due diligence?▼

A code-vs-comment ratio measures documentation density within a codebase. During technical due diligence, it acts as a maintainability signal to gauge documentation balance and engineering effort.

How do I estimate engineering effort for onboarding a new codebase?▼

Estimate engineering effort for onboarding by generating a defensible snapshot of the repository size and language mix. Analyzing lines of code and language breakdown provides a quick baseline for the work involved.

Why does my repository scan hang or take too long when counting lines of code?▼

Repository scans hang or take too long when analyzing dependency and build folders. You should skip common dependency directories using appropriate folder exclusions during the pygount analysis to ensure fast results.

Does pygount require excluding specific directories to accurately measure repository metrics?▼

Yes, pygount requires excluding dependency and build directories to accurately measure repository metrics. Skipping these folders prevents long scans and ensures the lines of code count reflects actual source code.

Can I use lines of code analysis to understand documentation density in my project?▼

Yes, you can use lines of code analysis to understand documentation density by calculating code-vs-comment ratios. This reveals the balance between functional code and explanatory comments across the detected languages.

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