{"uid":"cap_e_GHavYIxPkurBiNSjCBa","slug":"replenishment-timing-data-quality-suite-d14d83e3","name":"Replenishment Timing Data Quality Suite","description":"Replenishment timing — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied replenishment timing records (lead_days, received_units). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-retail-12-208?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_BdtyQzcW7qByPBqKwj2zw","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":"Audits, normalises strings, and fills missing values in replenishment timing records (lead_days, received_units) across up to 200 rows, returning 3 analytical reports.","exampleAgentPrompt":"Can you audit these 150 replenishment timing records for data quality issues, normalise the store_group strings, and fill any missing lead_days values using median imputation? Here are the rows with row_id, store_group, date, lead_days, and received_units.","exampleUseCases":[{"title":"Pre-load inventory data validation","prompt":"Before I import these replenishment timing records into our warehouse system, can you run a full quality audit on them, normalise the store_group strings, and fill any missing lead_days using median? I've got 120 rows with row_id, store_group, date, lead_days, and received_units."},{"title":"Supply chain lead time data cleaning","prompt":"I have 80 rows of supplier lead time data with some nulls in the lead_days column and inconsistent capitalisation in store_group — can you audit the record quality, fix the string formatting, and impute missing lead_days using the mean?"},{"title":"Received units gap filling for retail report","prompt":"My retail replenishment dataset has about 60 rows and several missing received_units values. Can you do a quality audit, normalise the store group names, and fill those gaps using zero imputation so I can prepare the report?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging issues across all rows, (2) a string normalisation report for store_group and text fields, and (3) an explicit missing-value fill report for the specified numeric field (lead_days or received_units) using the chosen method (zero, mean, or median). All reports are generated locally with no external lookups.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, store_group, date, lead_days, received_units) in any row — validation error","Invalid date format (not YYYY-MM-DD) — schema rejection","Invalid enum value for method or field — schema rejection","Numeric values outside allowed range (±1,000,000,000,000) — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need a combined data quality pass on replenishment timing records covering quality auditing, string normalisation, and missing-value imputation in a single call for up to 200 rows at low cost. It is best suited for offline batch data cleaning before ETL ingestion, not for real-time predictions or live inventory lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T12:52:58.896Z","isFirstParty":false,"canonicalSlug":"replenishment-timing-data-quality-suite-6b3c7e9e"}