{"uid":"cap_bLKbhu7jslyDtmPNiaw31","slug":"replenishment-timing-string-normalisation-missing-value-fill-2beab966","name":"Replenishment Timing String Normalisation & Missing-Value Fill","description":"Replenishment timing — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill 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-12-037?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_t0ehK5t1Q3oIkoBN02O4U","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 fills missing values (zero, mean, or median) in replenishment timing records containing lead_days and received_units, producing 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"Clean up my replenishment timing records — I have about 150 rows with store_group, date, lead_days, and received_units fields, and I need you to normalise the strings and fill missing lead_days values using the median method.","exampleUseCases":[{"title":"Imputing missing lead times before forecasting","prompt":"I have 120 replenishment timing rows for my retail stores with several null lead_days values — can you fill those missing values using the median and normalise the store group strings so I can feed this into my forecast model?"},{"title":"Cleaning received_units data for reporting","prompt":"My replenishment dataset has about 80 rows and some received_units entries are blank — fill those missing values with zero and normalise the string fields so the report is consistent across store groups."},{"title":"Standardising multi-store replenishment records","prompt":"I've got 200 rows of replenishment timing data across different store groups and dates, with messy string formatting and some null lead_days — run string normalisation and fill the nulls using the mean so everything is clean for our weekly retail review."}],"resultDescription":"Returns 2 analytical reports covering the submitted replenishment timing rows: one covering string normalisation of store_group and date fields, and one covering explicit missing-value imputation (zero, mean, or median) for the selected field (lead_days or received_units). Results are computed locally with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request exceeds maxItems limit","Invalid date format (must match YYYY-MM-DD pattern)","Missing required fields (row_id, store_group, date, lead_days, received_units)","Invalid method enum value (must be zero, mean, or median)","Invalid field enum value (must be lead_days or received_units)","Empty rows array — minItems is 1"],"whenToPreferThis":"Choose this endpoint when you have replenishment timing records (lead_days, received_units) with messy string formatting or missing numeric values and need a low-cost ($0.01 USDC), local analytical clean-up producing 2 reports for up to 200 rows — without requiring any live lookup, external prediction, or professional assessment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:34:15.940Z","isFirstParty":false,"canonicalSlug":"replenishment-timing-string-normalisation-missing-value-fill-2beab966"}