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To prevent over-engineering, enforce the YAGNI principle and a ladder of laziness framework, forcing the AI to prioritize standard libraries and native features over custom abstractions. This ensures the shortest working solution.

The YAGNI (You Ain't Gonna Need It) principle applies to clean code refactoring by strictly prohibiting speculative features and unnecessary abstractions. It prioritizes the simplest working implementation to maintain a minimal, highly maintainable codebase.

Enforce minimalist coding by applying an intensity-controlled strictness framework that dictates standard library usage first. This prevents writing new logic when existing native platform features or pre-built functions can achieve the same result.

Applying strict minimalism works for complex refactoring by systematically breaking down over-engineered logic into the shortest possible one-liners or minimal implementations. It ensures standard libraries replace unnecessary custom boilerplate.

A YAGNI-based approach to code optimization may limit foresight when building highly scalable systems, as strict adherence to the shortest immediate solution can sometimes omit necessary foundational architecture required for future complex extensions.

The ladder of laziness is a structured decision-making framework that forces developers to exhaust standard libraries, native platform features, and existing code before writing any new custom logic, ensuring maximal efficiency and minimal code.