review-tests

Automating test reviews involves discovering test files, mapping them to source modules, and running parallel agents to evaluate quality. This skill orchestrates that entire process, aggregating feedback into a structured summary report for efficient quality assurance.

Parallel processing for test review works by planning a parallelization strategy and concurrently launching multiple test-reviewer agents. This allows the system to evaluate multiple test files simultaneously, significantly reducing the time needed for comprehensive code quality assurance.

The best way to check unit test quality before merging is to use an automated review process. This skill discovers all relevant test files associated with the new feature, runs parallel reviews, and consolidates the findings into an actionable summary report.

Yes, automated test discovery can be used to identify Python test files in your project directory. The skill scans your current directory to find relevant tests, map them to their corresponding source modules, and prepare them for parallel review.

Yes, automated test review maps discovered test files directly to their corresponding source modules. This mapping ensures that the parallel review agents accurately assess test coverage and quality in the context of the actual application code.

You need parallel test review agents when you want efficient and scalable quality assurance across a large codebase. Using multiple agents concurrently to review tests solves the bottleneck of manual review, providing fast and structured feedback before code merges.