{"uid":"cap_rwq0rV_coVOTvMrlxsUv_","slug":"inventory-counts-date-by-category-pivot-3f6cf4ab","name":"Inventory Counts Date-by-Category Pivot","description":"Inventory counts - Date by category pivot: Date by category pivot for Inventory counts. Up to 200 supplied typed records. Local numeric and data-quality processing; no external enrichment or forecasting. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded date by category pivot deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-analytics-inventory-counts-pivot?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","warehouse","date","book_units","counted_units"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"warehouse":{"type":"string","default":"","maxLength":80},"book_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"counted_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","warehouse":"group-a","book_units":10,"counted_units":1},{"date":"2026-10-02","row_id":"row-2","warehouse":"group-b","book_units":20,"counted_units":2},{"date":"2026-10-03","row_id":"row-3","warehouse":"group-a","book_units":15,"counted_units":3},{"date":"2026-10-04","row_id":"row-4","warehouse":"group-b","book_units":30,"counted_units":4}],"maxItems":200,"minItems":1},"field":{"enum":["book_units","counted_units"],"type":"string","default":"book_units"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_q_psCiOBr2dcP8nYct0D8","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Pivots inventory count records by date and warehouse category, summarizing book_units or counted_units across up to 200 rows into a cross-tabulated matrix.","exampleAgentPrompt":"Take these inventory count records for our two warehouses — group-a and group-b — across October 2026 and produce a date-by-warehouse pivot table showing book_units for each date.","exampleUseCases":[{"title":"Warehouse discrepancy overview by date","prompt":"I have about 150 inventory count rows from our East and West warehouses over the past month. Can you pivot the counted_units by date and warehouse so I can see at a glance which dates have coverage gaps?"},{"title":"Book units matrix for cycle count review","prompt":"Here are our cycle count records from three warehouse groups for Q3. Pivot the book_units by date and warehouse category so our auditors can review the expected quantities across each location and date."},{"title":"Multi-location inventory trend summary","prompt":"We track inventory at four distribution centers daily. Can you take these 180 rows of count data and build a date-by-warehouse pivot on book_units so we can spot which centers are consistently short?"}],"resultDescription":"A date-by-category pivot table (cross-tabulation matrix) where rows represent dates and columns represent warehouse categories, with cells containing aggregated values of the selected field (book_units or counted_units). Derived locally from the supplied records with no external data enrichment.","failureModes":["Fewer than 1 row supplied — validation error","More than 200 rows submitted — request rejected at schema level","Invalid date format (not YYYY-MM-DD) — row-level or request-level error","Missing required fields (row_id, warehouse, date) — schema validation failure","All numeric values null — pivot may return empty or zero-filled cells","Duplicate row_ids — may cause unexpected aggregation behavior"],"whenToPreferThis":"Choose this endpoint when you have structured inventory count records (with dates, warehouse labels, and unit counts) and need a cross-tabulated pivot view for auditing, reporting, or comparison — especially when you want a lightweight, deterministic local computation without calling a full BI platform or data warehouse. Ideal for up to 200 records where no external enrichment or forecasting is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T19:12:26.797Z","isFirstParty":false,"canonicalSlug":"inventory-counts-date-by-category-pivot-3f6cf4ab"}