codebase-inspection

name	codebase-inspection
description	Inspect codebases w/ pygount: LOC, languages, ratios. Also: remote GitHub repo analysis via gh CLI API.
version	1.1.0
author	Hermes Agent
license	MIT
metadata	{"hermes":{"tags":["LOC","Code Analysis","pygount","Codebase","Metrics","Repository","GitHub","Deep Analysis","Repo Review"],"related_skills":["github-repo-management"]}}
triggers	["分析这个仓库","深度分析","repo analysis","what is this repo","代码行数","LOC","language breakdown","代码分析"]
prerequisites	{"commands":["pygount"]}

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

7. Remote GitHub Repo Analysis (no clone needed)

Use gh api to inspect a repo without cloning it — useful for quick assessment, comparison, or deciding whether to invest time in a deeper look.

# Repo overview (stars, forks, language, license, description)
gh repo view owner/repo --json name,description,stargazerCount,forkCount,primaryLanguage,createdAt,updatedAt,licenseInfo,homepageUrl

# List top-level files and directories
gh api repos/owner/repo/contents --jq '.[] | {name, type, size}'

# List files in a subdirectory
gh api repos/owner/repo/contents/path/to/dir --jq '.[] | {name, type, size}'

# Read a specific file (returns base64-encoded content)
gh api repos/owner/repo/contents/README.md --jq '.content' | base64 -d

# Read the first N lines of any file
gh api repos/owner/repo/contents/path/to/file.md --jq '.content' | base64 -d | head -100

# Count files by extension in a directory
gh api repos/owner/repo/contents/src --jq '[.[] | .name | split(".")[-1]] | group_by(.) | map({ext: .[0], count: length})'

# Check contributors
gh api repos/owner/repo/contributors --jq '.[] | {login, contributions}'

# Check recent commits
gh api repos/owner/repo/commits --jq '.[] | {sha: .sha[:8], message: .commit.message[:80], date: .commit.author.date}'

Deep Analysis Pattern (for user reports)

When the user asks to "deeply analyze" a GitHub repo:

1. Overview: gh repo view for metadata (stars, forks, language, license, dates)
2. Structure: gh api repos/.../contents for top-level directory layout
3. README: Read README.md for project purpose and usage
4. Key files: Read package.json, setup.py, Cargo.toml, or equivalent for dependencies/tech stack
5. Scripts/CI: Check .github/workflows/, scripts/, Makefile for build/test patterns
6. Specific deep dives: Read representative source files from key directories

Report format: Overview → Architecture → Tech Stack → Key Features → Strengths/Weaknesses → Comparison (if relevant).

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.