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Codebase inspection

Skill ed1son99/skills/skills/github/codebase-inspection

Inspect codebases w/ pygount: LOC, languages, ratios.From its SKILL.md

Install
npx -y skills add ed1son99/skills --skill codebase-inspection

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.4 KB, 867 tokens by cl100k_base, as published. Nobody here has run it

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

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:

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:

# 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

# 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

# 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

# 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.

What ships with it: 3 files

423 B alongside SKILL.md

Gives 0 of the 12 instructions most architecture codebase skills give in 867 tokens

Counted across 811 of the 1,134 authors here whose files we hold, read 2026-08-07

  • Ask the user which candidate to explorein 45 of 811, across 15 files
  • Apply the deletion test to suspected shallow modulesin 43 of 811, across 15 files
  • Read any relevant architecture decision records firstin 31 of 811, across 8 files
  • Use exact glossary terms in every suggestionin 30 of 811, across 10 files
  • Accept dependencies instead of creating themin 24 of 811, across 5 files
  • Include before and after visualisations for each candidatein 24 of 811, across 5 files
  • Read the domain glossary before exploringin 24 of 811, across 6 files
  • Return results instead of producing side effectsin 23 of 811, across 4 files
  • Explore the codebase for shallow modules and frictionin 23 of 811, across 3 files
  • Introduce seams only where things varyin 22 of 811, across 3 files
  • Reduce the number of methodsin 21 of 811, across 2 files
  • Design deep modules with small interfacesin 21 of 811, across 3 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 326,696. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.