Only the rows ImagineAI already writes for each generation: prompt, model, author, status, created_at and any token or credit column the plugin stores. No external API call is required to render the charts; everything sits in standard WordPress tables or a plugin-owned table on the same database.

Yes. Group by post_author with a Bar or Pie card and a Count aggregation to see who is actually using the plugin daily. Editorial leads typically pair this with an Area trend to spot adoption drift after training or onboarding rounds.

Yes. Group by created_at with an Area or Line card and a Sum aggregation on the tokens or credits column. The chart is useful for catching stuck loops, cost spikes and runaway prompts before they show up on the next monthly bill.

Yes. Group by the model column with a Pie or Bar card. The split is useful for understanding whether the team is anchored on one provider or spreading load across several, and for planning a model migration with a measurable starting point.

Yes. The table view and chart view sit on the same dataset, so a filter for a specific model, author or date range applies to both surfaces. Editors and ops can pivot between a row-level audit and a chart-level summary without rebuilding the filter.

Queries hit indexed columns on the generations table where possible. Expensive aggregations are cached per dashboard load, and filters use indexed fields like author, model and created_at, so usage dashboards stay quick even on installs with tens of thousands of generations.

Yes. Every SleekView dashboard is gated by WordPress capability, so an adoption-focused editorial dashboard can be visible to editors and team leads while finance sees a separate spend-focused view on the same underlying rows.

Yes. Any filtered set behind a chart card exports as CSV with the same columns the table view would show. Editorial leads typically use this for quarterly reviews, while finance pulls token-per-day CSVs to reconcile against vendor invoices.