codebase-inspection

Inspect and analyze codebases using pygount for LOC counting, language breakdown, and code-vs-comment ratios. Use when asked to check lines of code, repo size, language composition, or codebase stats.

5 stars

Best use case

codebase-inspection is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Inspect and analyze codebases using pygount for LOC counting, language breakdown, and code-vs-comment ratios. Use when asked to check lines of code, repo size, language composition, or codebase stats.

Teams using codebase-inspection should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/codebase-inspection/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/github/codebase-inspection/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/codebase-inspection/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How codebase-inspection Compares

Feature / Agentcodebase-inspectionStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Inspect and analyze codebases using pygount for LOC counting, language breakdown, and code-vs-comment ratios. Use when asked to check lines of code, repo size, language composition, or codebase stats.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Codebase Inspection with pygount

Analyze repositories for lines of code, language breakdown, file counts, and code-vs-comment ratios using `pygount`.

## When to Use

- User asks for LOC (lines of code) count
- User wants a language breakdown of a repo
- User asks about codebase size or composition
- User wants code-vs-comment ratios
- General "how big is this repo" questions

## Prerequisites

```bash
pip install --break-system-packages pygount 2>/dev/null || pip install pygount
```

## 1. Basic Summary (Most Common)

Get a full language breakdown with file counts, code lines, and comment lines:

```bash
cd /path/to/repo
pygount --format=summary \
  --folders-to-skip=".git,node_modules,venv,.venv,__pycache__,.cache,dist,build,.next,.tox,.eggs,*.egg-info" \
  .
```

**IMPORTANT:** Always use `--folders-to-skip` to exclude dependency/build directories, otherwise pygount will crawl them and take a very long time or hang.

## 2. Common Folder Exclusions

Adjust based on the project type:

```bash
# Python projects
--folders-to-skip=".git,venv,.venv,__pycache__,.cache,dist,build,.tox,.eggs,.mypy_cache"

# JavaScript/TypeScript projects
--folders-to-skip=".git,node_modules,dist,build,.next,.cache,.turbo,coverage"

# General catch-all
--folders-to-skip=".git,node_modules,venv,.venv,__pycache__,.cache,dist,build,.next,.tox,vendor,third_party"
```

## 3. Filter by Specific Language

```bash
# Only count Python files
pygount --suffix=py --format=summary .

# Only count Python and YAML
pygount --suffix=py,yaml,yml --format=summary .
```

## 4. Detailed File-by-File Output

```bash
# Default format shows per-file breakdown
pygount --folders-to-skip=".git,node_modules,venv" .

# Sort by code lines (pipe through sort)
pygount --folders-to-skip=".git,node_modules,venv" . | sort -t$'\t' -k1 -nr | head -20
```

## 5. Output Formats

```bash
# Summary table (default recommendation)
pygount --format=summary .

# JSON output for programmatic use
pygount --format=json .

# Pipe-friendly: Language, file count, code, docs, empty, string
pygount --format=summary . 2>/dev/null
```

## 6. Interpreting Results

The summary table columns:
- **Language** — detected programming language
- **Files** — number of files of that language
- **Code** — lines of actual code (executable/declarative)
- **Comment** — lines that are comments or documentation
- **%** — percentage of total

Special pseudo-languages:
- `__empty__` — empty files
- `__binary__` — binary files (images, compiled, etc.)
- `__generated__` — auto-generated files (detected heuristically)
- `__duplicate__` — files with identical content
- `__unknown__` — unrecognized file types

## Pitfalls

1. **Always exclude .git, node_modules, venv** — without `--folders-to-skip`, pygount will crawl everything and may take minutes or hang on large dependency trees.
2. **Markdown shows 0 code lines** — pygount classifies all Markdown content as comments, not code. This is expected behavior.
3. **JSON files show low code counts** — pygount may count JSON lines conservatively. For accurate JSON line counts, use `wc -l` directly.
4. **Large monorepos** — for very large repos, consider using `--suffix` to target specific languages rather than scanning everything.

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