{"uid":"cap_2XNdqgJxY6NhBpbviFDnl","slug":"replenishment-timing-string-normalisation-date-by-category-pivot-bd93d3a3","name":"Replenishment Timing String Normalisation + Date by Category Pivot","description":"Replenishment timing — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied replenishment timing records (lead_days, received_units). 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-12-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","lead_days","received_units"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"lead_days":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"store_group":{"type":"string","default":"","maxLength":80},"received_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","lead_days":10,"store_group":"group-a","received_units":1},{"date":"2026-10-02","row_id":"row-2","lead_days":20,"store_group":"group-b","received_units":2},{"date":"2026-10-03","row_id":"row-3","lead_days":15,"store_group":"group-a","received_units":3},{"date":"2026-10-04","row_id":"row-4","lead_days":30,"store_group":"group-b","received_units":4}],"maxItems":200,"minItems":1},"field":{"enum":["lead_days","received_units"],"type":"string","default":"lead_days"}}},"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_nFvANjnqku3KS_HYtSJIP","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 replenishment timing records: string normalisation and a date-by-category pivot, for up to 200 rows of lead_days or received_units data.","exampleAgentPrompt":"Can you normalise the store group strings and pivot lead_days by date and category for these replenishment records — I have about 50 rows with store_group, date, lead_days, and received_units fields?","exampleUseCases":[{"title":"Lead days pivot by store group","prompt":"I have 120 replenishment records with store groups like 'group-a' and 'group-b', dates from last quarter, and lead_days values — can you run string normalisation and pivot the lead_days field by date and category so I can see timing patterns across groups?"},{"title":"Received units category analysis","prompt":"Run the two analytical reports on my replenishment timing data — I want string normalisation and a date-by-category pivot for received_units across 80 rows covering our retail store groups from October 2026."},{"title":"Retail replenishment data cleanup","prompt":"My replenishment dataset has inconsistent store group labels and I need a clean pivot of received_units by date and store category — can you normalise the strings and produce the date pivot for these 30 records?"}],"resultDescription":"Two local analytical reports: (1) a string normalisation report cleaning and standardising the store_group field values, and (2) a date-by-category pivot table aggregating the selected numeric field (lead_days or received_units) broken down by date and store_group category — both derived from the supplied rows with no external data lookups.","failureModes":["Input exceeds 200 rows — request rejected","Invalid date format (must be YYYY-MM-DD) — validation error","Missing required fields (row_id, store_group, date, lead_days, received_units) — schema error","field enum value not recognised — validation error","Numeric values outside allowed range — schema rejection","Empty rows array — minimum items not met"],"whenToPreferThis":"Choose this endpoint when you need two quick, self-contained analytical reports — string normalisation plus a date-by-category pivot — on replenishment timing data without any live lookups or external predictions. Best suited for small-to-medium datasets (up to 200 rows) of retail replenishment records with store group, date, lead_days, and received_units fields, where the analysis runs entirely on supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T19:11:22.343Z","isFirstParty":false,"canonicalSlug":"replenishment-timing-string-normalisation-date-by-category-pivot-bd93d3a3"}