curate

Automated code quality reviews use correlation engines to combine structured history and git data, identifying stale decisions, knowledge gaps, and implicit dependencies to maintain your codebase. You execute a single command to scan and get an overview.

Architectural integrity ensures codebase decisions and dependencies remain aligned over time. Maintenance tools detect violations by correlating structured commit history with git data, flagging implicit dependencies and stale decisions that degrade structural quality.

Yes, you can identify implicit dependencies by running an automated review that applies correlation engines to your structured history and git data. This process surfaces hidden relationships and knowledge gaps within your codebase.

The best way to find knowledge gaps and stale decisions is through automated quality checks that cross-reference your codebase history with git commit data. This correlation engine approach catches overlooked architectural issues.

Yes, reviewing architectural integrity requires access to your complete codebase history and git commit data. The correlation engines depend on this structured data to accurately identify implicit dependencies and knowledge gaps.

Automated codebase correlation is not suited for projects lacking structured git history or commit data. Without accessible historical context, the correlation engines cannot effectively identify architectural decisions or implicit dependencies.