codebase-inspection

Analyze repositories to report lines of code, language breakdowns, and code-vs-comment ratios.

Updated May 21, 2026
One-click install
npx skills add https://github.com/samlaggz/hermes-agent-sam --skill codebase-inspection-samlaggz
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/samlaggz/hermes-agent-sam/tree/main/backup/skills/github/codebase-inspection
Command: npx skills add https://github.com/samlaggz/hermes-agent-sam --skill codebase-inspection-samlaggz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers quickly assess a repository's size and language composition by computing LOC, language breakdowns, and code-vs-comment ratios.

Core Features & Use Cases

  • LOC & Language Breakdown: Generate per-language file counts and code vs comments to understand project makeup.
  • Repo Audits: Suitable for one-off checks on a single project or inside monorepos to spot language balance and hotspots.
  • Use Case: Imagine evaluating a new codebase to estimate maintenance effort and identify dominant languages for tooling decisions.

Quick Start

Run pygount on a target repository to obtain a concise LOC and language report.

Frequently Asked Questions about codebase-inspection

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

FAQPage Schema
How do I analyze lines of code and language breakdowns in a repository?

Analyze repository lines of code and language breakdowns using pygount to generate per-language file counts and code-vs-comment ratios. The tool processes individual projects or monorepos to produce concise summary reports for quick codebase audits.

Can I exclude specific folders when calculating code vs comment ratios?

Yes, calculating code vs comment ratios supports customizable folder exclusions to filter out unwanted directories. Use pygount with language filters and folder exclusions to generate accurate LOC reports focusing only on relevant source files.

What is the best way to audit a monorepo for language balance and hotspots?

Auditing a monorepo for language balance and hotspots requires computing LOC and language breakdowns across the entire repository. Pygount analyzes monorepos to report per-language file counts and code-vs-comment ratios, identifying dominant languages and maintenance hotspots.

Does pygount work for multi-language codebase analysis across different programming languages?

Pygount works for multi-language codebase analysis by detecting and reporting language breakdowns across various programming languages. It generates per-language file counts and code-vs-comment ratios applicable to diverse projects for quick codebase reviews.

Why use lines of code analysis when evaluating a new codebase for maintenance effort?

Lines of code analysis helps evaluate a new codebase by estimating maintenance effort and identifying dominant languages for tooling decisions. LOC reports and language breakdowns provide insights into project makeup and code-vs-comment ratios for informed assessments.

What are the limitations of using LOC metrics for code quality assessment?

LOC metrics provide repository size and language composition data but do not directly measure code quality. Pygount generates lines of code, language breakdowns, and code-vs-comment ratios for structural analysis rather than functional code quality evaluation.