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rubberduck-doppelganger-hunt-pro

Detect semantic duplicates and clones in GitHub codebases using RubberDuck codebase intelligence.

Updated Apr 7, 2026
Search Tags:#code quality#duplicate detection#codebase management#semantic analysis#code duplication
One-click install
npx skills add https://github.com/faizanarshad/rubber_duck --skill rubberduck-doppelganger-hunt-pro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rubberduck-doppelganger-hunt-pro
Source: https://github.com/faizanarshad/rubber_duck/tree/main/.cursor/skills/rubberduck-doppelganger-hunt-pro
Command: npx skills add https://github.com/faizanarshad/rubber_duck --skill rubberduck-doppelganger-hunt-pro

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill identifies duplicate intent in code, helping developers avoid redundant implementations and maintain code quality.

Core Features & Use Cases

  • Semantic Duplicate Detection: Finds code that performs the same function but may be named differently or written in a different style.
  • Graph Dimension Analysis: Utilizes multiple dimensions (AST shape, control flow, data flow, etc.) to identify duplicates accurately.
  • Role Classification: Categorizes code into roles like canonical, hidden canonical, wrapper, etc., for better understanding and management.
  • Mergeability Analysis: Determines if duplicate code can be safely merged and provides a unification plan.
  • Use Case: If you have a large codebase with many similar functions, this Skill can help you identify and manage duplicates effectively.

Quick Start

Use the rubberduck-doppelganger-hunt-pro skill to find duplicate logic in the current repository.

Frequently Asked Questions about rubberduck-doppelganger-hunt-pro

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect semantic duplicates in a codebase?▼

Semantic duplicate detection finds functionally equivalent code across different names, shapes, files, and wrappers using graph dimension analysis like AST shape, control flow, and data flow.

How does semantic code clone detection work for similar functions?▼

Semantic clone detection analyzes graph dimensions including AST shape, control flow, and data flow to identify functionally equivalent code implementations and categorize their roles.

Do I need a GitHub repository to find duplicate code logic?▼

Yes, you need a GitHub repository and RubberDuck codebase intelligence setup to analyze and identify semantic duplicates and functionally equivalent code across your project.

What is the best way to manage duplicate code across different packages?▼

Role classification categorizes duplicate code into canonical, hidden canonical, and wrapper roles, while mergeability analysis determines if duplicates can be safely merged and provides a unification plan.

Can semantic analysis find duplicates between test and production code?▼

Yes, semantic duplicate detection identifies functionally equivalent code across different contexts including tests, production environments, inline implementations, and wrappers.

Why does duplicate code detection identify functions with different names as clones?▼

Semantic analysis identifies duplicates by intent and functional equivalence rather than exact text matching, detecting code that performs the same function even when named or styled differently.

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