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codebase-inspection

Calculate codebase metrics with pygount and output JSON or summaries.

1|Updated Apr 24, 2026
Search Tags:#codebase analysis#code metrics#repository audit#pygount#language breakdown#loc counting#code-comment ratio
One-click install
npx skills add https://github.com/automatedigital/spark --skill codebase-inspection-automatedigital
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/automatedigital/spark/tree/main/skills/github/codebase-inspection
Command: npx skills add https://github.com/automatedigital/spark --skill codebase-inspection-automatedigital

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually counting lines of code, identifying programming language composition, and calculating code-to-comment ratios for a repository is time-consuming and prone to error. This Skill automates these tasks to deliver accurate, actionable codebase metrics in seconds.

Core Features & Use Cases

  • Automated LOC and Language Breakdown: Uses pygount to scan any repository and generate a full breakdown of programming languages, file counts, and total lines of code.
  • Code-to-Comment Ratio Analysis: Calculates the proportion of executable code to documentation and comments to help assess codebase maintainability.
  • Flexible Output and Filtering: Supports filtering by specific file types or languages, and outputs results in summary tables, JSON, or per-file detailed lists for different use cases.
  • Use Case Example: A developer joining a new team can run this Skill to quickly understand the size and composition of the existing codebase, or a tech lead can use it to audit project metrics before a major refactor.

Quick Start

Use the codebase-inspection skill to analyze the target repository's total lines of code, language composition, and code-to-comment ratio.

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 count lines of code and get a language breakdown, this Skill automates scanning your target repository to calculate total lines of code, programming language composition, and code-to-comment ratios in seconds.

What is a code-to-comment ratio and how is it calculated for a codebase?▼

A code-to-comment ratio measures the proportion of executable code to documentation and comments within a codebase. This Skill calculates the ratio by analyzing repository files to help assess project maintainability.

Does pygount support excluding dependency and build directories during a repository audit?▼

Yes, when performing a repository audit with pygount, this Skill scans repository files and automatically excludes dependency and build directories to ensure accurate codebase metrics.

Can I filter codebase metrics by specific file types or programming languages?▼

Yes, you can filter codebase metrics by specific file types or programming languages. This Skill supports flexible filtering and outputs results in summary tables, JSON, or per-file detailed lists.

What output formats are available for language breakdown and LOC counting results?▼

For language breakdown and LOC counting results, available output formats include summary tables, structured JSON, and per-file detailed lists to support different project analysis use cases.

Do I need to install pygount to analyze project size and code metrics?▼

Yes, you need the pygount command-line tool installed to analyze project size and code metrics. This Skill requires pygount to scan repository files and generate structured metric outputs.

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