{"uid":"cap_4h6XHAlWKixGD-kKj3F5G","slug":"promotion-observations-string-normalisation-grouped-numeric-summary-ec3d5bda","name":"Promotion Observations — String Normalisation + Grouped Numeric Summary","description":"Promotion observations — String normalisation + Grouped numeric summary: String normalisation + Grouped numeric summary 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-05-023?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_sq2jx399EN_00clwjSO2p","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":"Normalises string fields and computes grouped numeric summaries for promotion observation records containing discount_amount and units_sold, returning two analytical reports.","exampleAgentPrompt":"Can you normalise the store group strings and give me a grouped numeric summary of discount_amount for these promotion records? I have about 50 rows with row_id, store_group, date, discount_amount, and units_sold fields — I'd like both reports at once.","exampleUseCases":[{"title":"Retail promotion discount analysis","prompt":"I've got 120 promotion observation rows with store_group, date, discount_amount, and units_sold. Can you clean up the store_group strings and produce a grouped numeric summary of discount_amount broken down by store group?"},{"title":"Units sold summary by store group","prompt":"Here are 80 rows of promotion data — each has a row_id, store_group, date, discount_amount, and units_sold. Normalise the string fields and give me a grouped summary of units_sold across the different store groups."},{"title":"Data cleanup for promotion campaign report","prompt":"Before I put together our campaign report, can you normalise the store_group labels and compute grouped numeric stats on both discount_amount and units_sold for these 200 promotion records?"}],"resultDescription":"Two analytical reports: (1) a string normalisation report cleaning fields like store_group, and (2) a grouped numeric summary of the selected field (discount_amount or units_sold) aggregated by group — covering up to 200 input rows with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, store_group, date, discount_amount, units_sold) causes validation error","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Numeric values outside allowed range cause rejection","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and grouped numeric aggregation on promotion data (discount_amount, units_sold) in a single low-cost call. Ideal for small-to-medium retail datasets (up to 200 rows) where you want two bundled analytical reports without setting up a full data pipeline. Best suited for offline/batch analytical use rather than real-time prediction or live data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:49:08.448Z","isFirstParty":false,"canonicalSlug":"promotion-observations-string-normalisation-grouped-numeric-summary-ec3d5bda"}