codebase-recon

codebase-recon-skill

A coding agent skill that analyzes git history to understand a codebase before reading any code. Reveals project health, risk areas, team structure, and development momentum.

Inspired by "The Git Commands I Run Before Reading Any Code" by Ally Piechowski.

Installation

Via skills.sh (works with 20+ coding agents)

npx skills add yujiachen-y/codebase-recon-skill

Works with Claude Code, Cline, Cursor, GitHub Copilot, Gemini CLI, and any agent supporting the Agent Skills Specification.

Via Claude Code plugin system

/plugin marketplace add yujiachen-y/codebase-recon-skill

Then install the plugin from the marketplace browser via /plugin.

Via Codex plugin system

codex plugin marketplace add yujiachen-y/codebase-recon-skill

Then run /plugins in Codex, choose the Codebase Recon marketplace, and install codebase-recon.

Usage

In your coding agent, invoke:

/codebase-recon

The skill will:

1. Probe the repo to determine its scale (small / medium / large)
2. Analyze 7 dimensions in parallel: code hotspots, bus factor, bug magnets, team momentum, firefighting frequency, recently added files, and active contributors
3. Cross-reference hotspots with bug magnets to identify high-risk files
4. Report findings with actionable recommendations

What You'll Learn

Dimension	Question Answered
Code Hotspots	Which files change the most?
Bug Magnets	Which files attract the most bug fixes?
High-Risk Files	Which files are both hot AND buggy?
Bus Factor	Who knows what? Is knowledge concentrated?
Team Momentum	Is development accelerating, stable, or declining?
Firefighting	How often are there emergency fixes and reverts?
Recently Added	Where is active development happening?

Requirements

• git (any version)
• A git repository with commit history

Attribution

This skill is inspired by "The Git Commands I Run Before Reading Any Code" by Ally Piechowski. The original article describes 5 git commands for codebase reconnaissance. This skill extends the concept with auto-scaling, cross-referencing, and actionable recommendations.

License

MIT