Anywhere structured. Google Sheets and Notion suit editorial teams without engineers; JSON files in the theme suit static archives; REST endpoints suit teams running a recipe service. SleekRank reads each of these via the matching source type.

Yes. Add a cooker-type column and split related-recipe clusters by that field. Some recipes apply to both; others (recipes that need very low pressure or a precise sauté step) only run on one cooker type, and the filter keeps the clusters honest.

Yes. A meta mapping pointing at a JSON-LD script tag produces a full Recipe schema block per page, including name, recipeIngredient, recipeInstructions with step timings, recipeYield, and totalTime drawn from the row. Google's recipe rich results pick it up cleanly.

Store release type as a column and pressure-down minutes as a separate column. A selector mapping drops both into the sidebar and the schema's totalTime calculation, so each page reflects realistic end-to-end timing.

Yes. Add a serving-size convention to the row and render a unit toggle in the base template that scales quantities client-side. The crawler still sees the base quantities, which keeps schema and visible content consistent.

Edit the relevant column on one shared lookup row or update the JSON map of brand terms, and every page that uses that terminology updates on the next cache refresh. No editor pass across the published corpus required.

Yes. Add an affiliate-link column keyed by cooker size or brand, and a selector mapping drops the right link into the equipment block per page. Updating an affiliate URL touches one row instead of every published post.

The source system owns history. Google Sheets keeps version history, Notion tracks edits, and JSON in git carries commit history. SleekRank reads current state on each cache cycle, so a test-kitchen change rolls out site-wide on the next refresh.