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codebase-inspection

Analyze repository composition with pygount to measure code size and language distribution.

Updated May 5, 2026
Search Tags:#codebase#code metrics#repository analysis#loc#pygount#language breakdown
One-click install
npx skills add https://github.com/yanochka11/harness_bro --skill codebase-inspection-yanochka11
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-inspection
Source: https://github.com/yanochka11/harness_bro/tree/main/.claude/skills/ported/codebase-inspection
Command: npx skills add https://github.com/yanochka11/harness_bro --skill codebase-inspection-yanochka11

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers quickly understand repository size, structure, and composition without manually counting files, lines, or language usage.

Core Features & Use Cases

  • Repository Metrics Analysis: Calculates lines of code, file counts, language breakdowns, and code-to-comment ratios using pygount.
  • Flexible Inspection Workflows: Supports full repository scans, language-specific analysis, and detailed file-level reporting with recommended exclusions.
  • Use Case: When evaluating an unfamiliar machine learning repository, use this Skill to identify its scale, dominant programming languages, and overall code composition before making changes.

Quick Start

Ask the codebase-inspection skill to analyze this repository and report its lines of code, languages, and file statistics.

Frequently Asked Questions about codebase-inspection

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

FAQPage Schema
How do I measure lines of code and language breakdown in a repository?▼

Repository metrics analysis calculates lines of code, file counts, language breakdowns, and code-to-comment ratios using pygount. It scans the codebase to generate reliable statistics for code reviews and project sizing.

How do I analyze code size for an unfamiliar codebase before making changes?▼

Analyzing code size for an unfamiliar codebase involves scanning the repository to identify its scale, dominant programming languages, and overall code composition. This helps you understand project structure before modifying any files.

Do I need pygount installed to generate code and comment statistics?▼

Yes, you need pygount installed to generate reliable code, comment, and file statistics. The codebase inspection process relies on pygount to accurately calculate lines of code and code-to-comment ratios.

Can I run language-specific analysis and exclude certain folders during a repository audit?▼

Yes, you can run language-specific analysis and exclude certain folders during a repository audit. Configuring folder exclusions ensures reliable statistics by filtering out non-essential directories from the full repository scan.

What is the best way to audit a codebase without manually counting files and lines?▼

The best way to audit a codebase without manually counting files and lines is to use an automated repository metrics tool. It calculates code size, file statistics, and language distribution quickly for comprehensive project sizing.

What are the limitations when scanning repository composition for project sizing?▼

Limitations when scanning repository composition for project sizing include the need for configured folder exclusions to ensure reliable statistics. Without proper exclusions, generated code and file counts may be inaccurate.

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