ck:autoresearch

Automate test coverage improvement by running an autonomous iterative optimization loop that executes N iterations, automatically keeping or discarding code changes based on verification results.

Autonomous iterative optimization automates experimentation and improvement in codebase metrics like test coverage, performance, and bundle size by learning from git history to refine code changes.

Yes, a git repository is required for autonomous code optimization loops because the iteration process learns from git history and automatically keeps or discards experimental changes.

Configure regression checks by specifying a guard command in your configuration, such as running TypeScript type checking and Jest tests, to ensure no regressions occur during the optimization iterations.

Yes, autonomous optimization supports reducing bundle size because the iterative loop is designed to target and improve measurable codebase metrics including test coverage, performance, and bundle size.

Limitations include the requirement for specific metric verification configuration, a git repository, and regression checks, making it unsuitable for codebases lacking measurable mechanical metrics.