Show HN: Brooks-Lint – AI code reviews grounded in 12 classic engineering books (github.com)

Brooks-Lint is a groundbreaking AI tool for code reviews, inspired by insights from twelve classic engineering books such as "The Mythical Man-Month" and "Code Complete." Unlike traditional code quality tools that mainly assess syntactic issues, Brooks-Lint evaluates code against six decay risk dimensions—such as Cognitive Overload and Dependency Disorder—synthesizing structured findings alongside citations and actionable remedies. This approach not only enhances the quality of code reviews but also offers a more profound understanding of common architectural and design pitfalls, significantly empowering developers to improve code maintainability and robustness. For the AI/ML community, Brooks-Lint represents a significant leap forward in automated code quality evaluation. Its ability to produce detailed reports—including severity labels and a health score—means developers can quickly identify areas needing attention and understand the source of specific issues. Furthermore, it visualizes dependencies and risks through a Mermaid dependency graph, enhancing clarity for complex architectures. With tests showing a 94% overall pass rate versus only 16% for current AI models, Brooks-Lint exemplifies how AI can deliver consistent, traceable, and insightful diagnostics that surpass traditional code analysis tools.