AI Code Review Checklist Generator

Checklists prevent the most common code review failures: inconsistency (different reviewers catch different things), fatigue (missing issues in large PRs), and blind spots (forgetting to check security or performance). A structured checklist ensures every review covers the essential categories systematically, regardless of who is reviewing or how large the change is.

The AI generates checklist items specific to your technology choices. A React PR gets items about hook dependencies, memoization, and accessibility. A Node.js API gets items about input validation, error handling, and N+1 queries. A database migration gets items about backwards compatibility, rollback plans, and data integrity. Generic items are supplemented with stack-specific concerns.

The checklist covers correctness (logic errors, edge cases), security (injection, auth, data exposure), performance (queries, caching, memory), readability (naming, complexity, documentation), error handling (graceful failures, logging), testing (coverage, edge cases, mocks), and deployment (migrations, feature flags, rollback). Additional categories are added based on your change type.

Use the focus areas field to add your team's specific concerns — things like compliance requirements, accessibility standards, or performance budgets. The AI incorporates these into the checklist alongside standard best practices. Over time, you can build a library of checklists for different change types that reflect your team's accumulated knowledge.

Quick reviews cover essentials: correctness, obvious security issues, and test coverage. Standard reviews add performance, readability, and error handling checks. Deep reviews include security threat modeling, performance profiling considerations, concurrency analysis, and edge case scenarios. Choose based on the risk and complexity of the change — critical auth changes deserve deep reviews.