review-sprint

Automate a post-implementation code review by running a mechanical audit that analyzes changed files, test suites, and sprint specifications to identify dead code, spec-implementation drift, and test naming issues.

A mechanical code audit catches dead code, incorrect test naming, spec-implementation drift, workspace anomalies, pre-commit compliance failures, and demo script execution errors before manual verification.

Yes, you need git, pytest, and pre-commit configured in your environment to execute the automated code quality audit and validate your sprint changes against project principles.

Spec-implementation drift detection works by analyzing sprint specifications alongside changed files and test suites to verify that the implemented code adheres to the predefined project criteria.

You can integrate this mechanical audit into testing automation pipelines by running it after sprint implementation to systematically flag code quality issues and workspace anomalies before verification.

The best way to check for dead code and test naming discrepancies is performing a systematic mechanical audit that analyzes changed files against predefined criteria to ensure code quality.