plan-eng-review

You can automate an architecture and code review by running the skill to analyze system design, component boundaries, dependency graphs, and data flow patterns. It evaluates code organization, DRY violations, and error handling to identify potential issues before implementation.

Outside voice integration in AI code review provides a second opinion from a different AI system. This cross-model coverage ensures a more comprehensive evaluation of your architecture and performance metrics by leveraging diverse analytical perspectives.

Identify N+1 queries and slow code paths by running an automated performance review that analyzes memory usage concerns and caching opportunities. The process requires access to your project's performance metrics to pinpoint inefficiencies accurately.

Yes, you can detect test framework coverage and trace code paths automatically. The test review evaluates complete test coverage, maps user flows, and traces code paths to ensure all critical components are validated against your project dependencies.

Yes, you need access to performance metrics and test frameworks to run a code quality review. A thorough understanding of the project's codebase and dependencies is required to accurately evaluate technical debt and over-engineering.

The best way to review technical debt and over-engineering is through an automated code quality review. It evaluates code organization, DRY violations, and under-engineering to pinpoint exact areas requiring refactoring before implementation.