{"uid":"cap_YCkg5hGJ-pGk7rtpUDdNn","slug":"promotion-observations-record-quality-audit-date-by-category-pivot-30db39b9","name":"Promotion Observations — Record Quality Audit + Date by Category Pivot","description":"Promotion observations — Record quality audit + Date by category pivot: Record quality audit + Date by category pivot for supplied promotion observations records (discount_amount, units_sold). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-retail-05-017?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","discount_amount","units_sold"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"units_sold":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"store_group":{"type":"string","default":"","maxLength":80},"discount_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","units_sold":1,"store_group":"group-a","discount_amount":10},{"date":"2026-10-02","row_id":"row-2","units_sold":2,"store_group":"group-b","discount_amount":20},{"date":"2026-10-03","row_id":"row-3","units_sold":3,"store_group":"group-a","discount_amount":15},{"date":"2026-10-04","row_id":"row-4","units_sold":4,"store_group":"group-b","discount_amount":30}],"maxItems":200,"minItems":1},"field":{"enum":["discount_amount","units_sold"],"type":"string","default":"discount_amount"}}},"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_x7dSvdjdmOk50g1WRZjGW","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":"Runs two local analytical reports on promotion observation data (discount_amount, units_sold): a record quality audit and a date-by-category pivot table, for up to 200 rows.","exampleAgentPrompt":"Can you run a record quality audit and a date-by-category pivot on my promotion data? I have rows with store_group, date, discount_amount, and units_sold — use discount_amount as the pivot field. Here are up to 200 rows of promotion observations I need analyzed.","exampleUseCases":[{"title":"Retail promotion data quality check","prompt":"I have a batch of promotion records from our last campaign — each row has a store group, date, discount amount, and units sold. Can you audit them for quality issues like missing values or bad data, and also give me a pivot of discount amounts by date and store group? I'll send you up to 150 rows."},{"title":"Pre-analysis validation for discount data","prompt":"Before I run our promo analysis, can you check these 80 promotion observation rows for record quality problems and also pivot the discount_amount field by date and category so I can see the breakdown by store group?"},{"title":"Units sold pivot for category reporting","prompt":"I need a date-by-category pivot table of units_sold across these promotion records, plus a quality audit to flag any bad rows — I have about 200 rows with store_group, date, discount_amount, and units_sold fields."}],"resultDescription":"Returns two analytical reports: (1) a record quality audit identifying missing, null, or malformed values in the submitted promotion rows, and (2) a date-by-category pivot summarizing the selected numeric field (discount_amount or units_sold) grouped by date and store_group, both computed locally on the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, store_group, date, discount_amount, units_sold) on any row causes validation error","Invalid date format (not YYYY-MM-DD) triggers schema rejection","Field enum value not one of 'discount_amount' or 'units_sold' causes error","Empty rows array rejected by minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you need both a data quality audit and a date-by-category pivot on promotion (retail discount/sales) data in a single low-cost call. It is ideal for pre-analysis validation workflows where you want to flag bad records and summarize numeric fields by date and store group simultaneously, without any external API calls or live data lookups. Best suited for batches up to 200 rows of structured promotion observations.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:50:10.503Z","isFirstParty":false,"canonicalSlug":"promotion-observations-record-quality-audit-date-by-category-pivot-30db39b9"}