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LamseyahElias
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codebase-inspection

Identify codebase size and composition using pygount metrics.

Updated Jun 11, 2026
Search Tags:#codebase#repository-analysis#loc#code-metrics#pygount#language-breakdown
One-click install
npx skills add https://github.com/LamseyahElias/jarvis-cloud-v2 --skill codebase-inspection-lamseyahelias
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/LamseyahElias/jarvis-cloud-v2/tree/main/hermes-agent/skills/github/codebase-inspection
Command: npx skills add https://github.com/LamseyahElias/jarvis-cloud-v2 --skill codebase-inspection-lamseyahelias

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quickly quantify a codebase's size and composition by using pygount to generate LOC, language breakdowns, and code-vs-comment ratios without manual inspection.

Core Features & Use Cases

  • Automated codebase metrics: LOC, language distribution, and file counts for repositories.
  • Exclusion-aware analysis: Skips common directories to avoid noisy results and long runtimes.
  • Dashboard-ready outputs: Produces concise metrics suitable for quick assessments and reporting.

Quick Start

Run this skill against your codebase to obtain a concise LOC, language breakdown, and repository metrics.

Frequently Asked Questions about codebase-inspection

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

FAQPage Schema
How do I get a language breakdown and LOC metrics for a codebase?▼

You can analyze codebase composition by running pygount metrics to automatically generate LOC, language distribution, file counts, and code-vs-comment ratios without manual inspection.

Can I calculate code-vs-comment ratios for repositories written in any programming language?▼

Yes, you can apply this analysis to repositories of any language. It produces a language breakdown and code-vs-comment ratios by identifying the size and composition of the codebase.

How do I exclude directories to avoid noisy results when analyzing repository metrics?▼

The analysis applies sensible folder exclusions to skip common directories, preventing noisy results and long runtimes while generating concise codebase metrics.

What is the best way to generate dashboard-ready code metrics for a repository assessment?▼

Using pygount to analyze codebases is a highly effective way to produce dashboard-ready outputs, returning structured metrics like LOC and language breakdowns suitable for quick reporting.

Do I need pygount installed to analyze codebase size and composition?▼

Yes, you need pygount and standard Python tooling installed. The skill relies on pygount metrics to quantify codebase size and generate structured metrics for dashboards and assessments.

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