Does this skill automatically fix the identified code smells?

It focuses on identification and root cause analysis. While it can suggest refactoring patterns, it encourages developer understanding over automated code generation to prevent new smells.

Based on what standards does this tool analyze code?

The skill is grounded in established industry standards, specifically Martin Fowler's Refactoring catalog, SWEBOK, and principles from Michael Feathers.

What specific code smells can this skill detect?

It identifies various issues including long methods (>20 lines), duplicated logic, long parameter lists, data clumps, primitive obsession, and lazy classes.

How does this differ from a standard linter?

Linters typically catch syntax and style errors; this skill looks for deeper architectural 'smells' that indicate long-term maintenance problems and design flaws.

Code Smell Detector

Name: Code Smell Detector
Author: sethdford

bysethdford

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Developer Tools

Analyzes codebases to identify maintainability issues and design flaws like duplication, long methods, and high coupling.

The Code Smell Detector is a specialized skill for Claude Code designed to evaluate the health and maintainability of your source code. Drawing on industry-standard heuristics from Martin Fowler's refactoring catalog, it scans for problematic patterns such as excessive method length, duplicated logic, and primitive obsession. This tool is essential for technical debt assessments and peer reviews, helping engineers transition from code that simply works to code that is clean, scalable, and easy to modify.

Key Features

01Flags high coupling and low cohesion in classes and modules to improve modularity

02Identifies duplicated code blocks to prevent maintenance overhead and bug multiplication

03Detects long methods and complex logic violating the Single Responsibility Principle

04Spotlights Primitive Obsession and Data Clumps for better domain abstraction

05Analyzes switch statements for potential replacement with polymorphic patterns

065 GitHub stars

Use Cases

01Performing automated technical debt audits on legacy or inherited codebases

02Identifying high-priority refactoring candidates before adding new feature sets

03Providing objective maintainability feedback during code reviews and PR evaluations

Key Features

01Flags high coupling and low cohesion in classes and modules to improve modularity

02Identifies duplicated code blocks to prevent maintenance overhead and bug multiplication

03Detects long methods and complex logic violating the Single Responsibility Principle

04Spotlights Primitive Obsession and Data Clumps for better domain abstraction

05Analyzes switch statements for potential replacement with polymorphic patterns

065 GitHub stars

Use Cases

01Performing automated technical debt audits on legacy or inherited codebases

02Identifying high-priority refactoring candidates before adding new feature sets

03Providing objective maintainability feedback during code reviews and PR evaluations