data-optimize

To audit your data layer for performance bottlenecks, run an automated check against a stack-aware checklist that detects N+1 queries, pagination problems, and real-time listener leaks. This generates a ranked roadmap of actionable optimization items.

Data-layer stack detection automatically identifies your project's data-layer stack and loads stack-specific pivots from installed sc-* plugins. This stack-aware approach allows the audit to pinpoint exact query performance issues and suggest targeted improvements.

You can generate a ranked roadmap to fix N+1 queries and excessive payload sizes by executing an automated data-layer optimization audit. This process evaluates your stack and outputs a prioritized list of actionable items for performance improvement.

Yes, you need Python and various data-layer SDKs installed to run this data-layer performance audit. These dependencies are required to automatically detect your stack and load the corresponding sc-* plugins for stack-specific optimization.

You can use this data optimization audit with frameworks supported by installed sc-* plugins. The Skill automatically detects your data-layer stack and loads stack-specific pivots, extending audit compatibility to your specific environment.