evidence-heavy-evaluator

Automate repository evaluation by generating evidence-first reports with scored criteria and actionable recommendations. This approach performs deterministic repo evaluations for release readiness, maintainability, and overall code health.

Evidence-first reporting in code evaluation generates reports based on collected evidence, ensuring objectivity and deterministic scoring. It provides consistent, repeatable evaluation results with specific suggestions for code improvement.

Yes, you need Python 3 and the uv package manager to run repository analysis scripts. These dependencies are required to execute the scripts that generate the deterministic evaluation and readiness reports.

Yes, you can optionally execute lint, test, and build checks during a repository evaluation. The evaluation supports adjustable depth and profiles for readiness, maintainability, and release-readiness.

Assess project maintainability before a major launch by using a deterministic evaluator to generate a comprehensive report highlighting potential issues and their severity. This provides auditable artifacts and actionable recommendations.