multi-axis-review

Automated code reviews use structured severity ratings across multiple axes like correctness and security to generate machine-parsable feedback. This eliminates inconsistent ad-hoc feedback and provides reliable output for CI pipelines and merge bots to gate pull requests.

Severity-rated diff analysis evaluates pull request changes across 10 quality dimensions including correctness, security, and performance. It returns a standardized, machine-parsable report that allows CI pipelines and automated PR loops to automatically gate merges on critical issues.

The best way to catch cross-cutting issues is applying a holistic approach gate before line-level review to catch fatal strategic flaws, followed by a structured 10-axis evaluation covering maintainability and other critical code quality dimensions with clear severity ratings.

Yes, automated PR review generates a standardized, machine-parsable report with severity ratings for every finding. Merge bots and downstream tooling can reliably parse this structured output to automatically block merges when critical code quality issues are detected.

You need machine-parsable code review output when integrating reviews into automated PR loops or CI pipelines. It ensures downstream merge bots can reliably parse severity-rated findings to gate merges, replacing unstructured feedback that automated tools cannot read.

Yes, multi-axis-review operates with zero external dependencies, directly analyzing attached pull request diffs to produce structured severity-rated review reports for downstream merge tooling consumption.