ai-smell-fix

Code smells in AI-generated code include over-abstraction, defensive paranoia, comment spam, speculative features, enterprise patterns in simple code, generic wrapper abuse, verbose naming, excessive structure, and architectural bloat. These patterns reduce code readability and maintainability.

Fix verbose naming and excessive structure by running a refinement process that identifies architectural bloat and simplifies it, stripping away unnecessary enterprise patterns to improve overall code quality and human readability.

Yes, this approach works for speculative features by identifying and removing unnecessary abstractions and generic wrapper abuse across targeted directories, ensuring the remaining codebase adheres to human coding standards and best practices.

The best way to humanize AI-generated code is to target specific directories and automatically remove AI patterns like enterprise patterns in simple code, generic wrapper abuse, and architectural bloat to achieve a human-written appearance.

You should avoid using an AI smell fix refactoring approach on codebases that genuinely require deep enterprise patterns, defensive paranoia, or extensive architectural structure, as simplifying these necessary abstractions would degrade code quality.