code-review-and-quality

Multi-axis code review evaluates pull request diffs across correctness, readability, architecture, security, and performance. You provide the changed files and request findings to receive severity-classified feedback and actionable fixes before merging.

Reviewing AI-generated code requires validating the verification story across five axes: correctness, readability, architecture, security, and performance. Apply a systematic checklist to the diff to classify findings by severity and ensure safer merges.

Classify code review findings by severity by walking the refactor diff with a checklist covering correctness, architecture, and security. This categorization produces prioritized, actionable feedback for the verification story.

Yes, multi-axis code review applies to refactors and bug fixes by assessing change sizes and dependency discipline. It guides splitting strategies to reduce review friction and validate the overall verification story before merge.

Evaluating the verification story improves code quality by checking tests and specifications against the diff. This prevents unverified changes from entering the main branch by ensuring correctness and performance standards are met.

Before a merge, run security and performance checks as part of a five-axis evaluation covering correctness, readability, architecture, security, and performance. This systematic assessment identifies vulnerabilities and bottlenecks in the changed files.