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codebase-inspection

Analyze repositories to produce LOC, language breakdown, and code-vs-comment metrics using pygount.

Updated Apr 9, 2026
Search Tags:#codebase#analysis#metrics#repository#languages#loc#pygount
One-click install
npx skills add https://github.com/nadicodeai/argo-agent --skill codebase-inspection-nadicodeai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/nadicodeai/argo-agent/tree/main/skills/github/codebase-inspection
Command: npx skills add https://github.com/nadicodeai/argo-agent --skill codebase-inspection-nadicodeai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Inspect codebases to quantify size, language mix, and code-vs-comment distribution using pygount, enabling quick assessments of project composition.

Core Features & Use Cases

  • LOC & Language Breakdown: compute lines of code per language and file counts for a repository.
  • Code-vs-Comment Ratios: surface how code density varies by language and project, aiding refactor decisions.
  • Use Case: When planning a large tech debt cleanup, run this to identify primary languages and hotspots that drive complexity.

Quick Start

Navigate to your repository and run the pygount-based analysis to generate a metrics 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 by language in a multi-language repository?▼

To count lines of code by language in a repository, you can run a pygount-based analysis that computes LOC and file counts per language. This generates a metrics report detailing the exact language breakdown.

What is the best way to measure code-vs-comment ratios across a large monorepo?▼

Measuring code-vs-comment ratios in a monorepo involves analyzing the repository with pygount to surface code density variations by language. This helps identify project composition and hotspots driving complexity for refactor decisions.

Can I exclude dependency folders when analyzing repository metrics?▼

Yes, you can exclude dependency folders when analyzing repository metrics. The analysis supports configurable folder exclusions to skip dependency directories, ensuring the LOC and language breakdown only reflects your actual project code.

Does repository LOC analysis with pygount require a specific Python environment?▼

Yes, repository LOC analysis with pygount requires pygount installed in a Python environment and a shell. You must navigate to the repository directory in the shell before running the analysis to generate the metrics report.

When should I use codebase metrics analysis for tech debt cleanup?▼

You should use codebase metrics analysis for tech debt cleanup when you need to quantify project size and identify primary languages. It surfaces code density variations by language, helping pinpoint hotspots that drive complexity.

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