everything-claude-code/skills/repo-scan/SKILL.md

name, description, origin, tools

name	description	origin	tools
repo-scan	Cross-stack source code asset audit — classifies every file, detects embedded third-party libraries, and delivers actionable four-level verdicts per module with interactive HTML reports.	https://github.com/haibindev/repo-scan	file analysis dependency detection HTML report generation

repo-scan

Every ecosystem has its own dependency manager, but no tool looks across C++, Android, iOS, and Web to tell you: how much code is actually yours, what's third-party, and what's dead weight.

When to Activate

• Taking over a large legacy codebase and need a structural overview
• Before major refactoring — identify what's core, what's duplicate, what's dead
• Auditing third-party dependencies embedded directly in source (not declared in package managers)
• Preparing architecture decision records for monorepo reorganization

Installation

# Copy into your Claude Code skills directory
git clone https://github.com/haibindev/repo-scan.git
cp -r repo-scan ~/.claude/skills/repo-scan

Review the source before installing any agent skill.

Core Capabilities

Capability	Description
Cross-stack scanning	C/C++, Java/Android, iOS (OC/Swift), Web (TS/JS/Vue) in one pass
File classification	Every file tagged as project code, third-party, or build artifact
Library detection	50+ known libraries (FFmpeg, Boost, OpenSSL…) with version extraction
Four-level verdicts	Core Asset / Extract & Merge / Rebuild / Deprecate
HTML reports	Interactive dark-theme pages with drill-down navigation
Monorepo support	Hierarchical scanning with summary + sub-project reports

Analysis Depth Levels

Level	Files Read	Use Case
fast	1-2 per module	Quick inventory of huge directories
standard	2-5 per module	Default audit with full dependency + architecture checks
deep	5-10 per module	Adds thread safety, memory management, API consistency
full	All files	Pre-merge comprehensive review

Workflow

1. Scan: /repo-scan runs a pre-scan pass to classify files and detect dependencies
2. Analyze: AI reviews the structured data and assigns verdicts per module
3. Report: Interactive HTML report generated with verdict distribution and drill-down

Real-World Example

On a 50,000-file C++ monorepo:

• Found FFmpeg 2.x (2015 vintage) still in production
• Discovered the same SDK wrapper duplicated 3 times
• Identified 636 MB of committed Debug/ipch/obj build artifacts
• Classified: 3 MB project code vs 596 MB third-party

Best Practices

• Start with standard depth for first-time audits
• Use fast for monorepos with 100+ modules to get a quick inventory
• Run deep incrementally on modules flagged for refactoring
• Review the cross-module analysis for duplicate detection across sub-projects

Links

• GitHub Repository