{"uid":"cap_cp5-fQ1S82AePEHdvNRWR","slug":"damaged-stock-string-normalisation-and-secondary-measure-weighted-mean-226fd1cb","name":"Damaged Stock String Normalisation and Secondary-Measure Weighted Mean","description":"Damaged stock — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied damaged stock records (handled_units, damaged_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-18-034?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","handled_units","damaged_units"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"store_group":{"type":"string","default":"","maxLength":80},"damaged_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"handled_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","store_group":"group-a","damaged_units":1,"handled_units":10},{"date":"2026-10-02","row_id":"row-2","store_group":"group-b","damaged_units":2,"handled_units":20},{"date":"2026-10-03","row_id":"row-3","store_group":"group-a","damaged_units":3,"handled_units":15},{"date":"2026-10-04","row_id":"row-4","store_group":"group-b","damaged_units":4,"handled_units":30}],"maxItems":200,"minItems":1},"field":{"enum":["handled_units","damaged_units"],"type":"string","default":"handled_units"}}},"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_DHqMTQ3510dpNr0ry6NgJ","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 store/date strings and computes a secondary-measure weighted mean across up to 200 damaged-stock records (handled_units, damaged_units) in two bundled analytical reports.","exampleAgentPrompt":"Can you normalize the store group strings and compute a handled_units weighted mean across these damaged stock rows? I have about 50 records with row_id, store_group, date, handled_units, and damaged_units — I need the two bundled reports.","exampleUseCases":[{"title":"Retail damage rate analysis by store","prompt":"I have 120 rows of damaged stock data from our warehouse, each with a store_group, date, handled_units, and damaged_units. Can you normalize the store group labels and give me a weighted mean of damaged_units using handled_units as the weight?"},{"title":"Cleaning inconsistent store group names","prompt":"Our store group labels are messy — some are uppercase, some have trailing spaces. Run string normalization on my damaged stock records so the grouped analysis is consistent, and also compute the weighted mean for the handled_units field across all 80 rows."},{"title":"Monthly inventory quality report","prompt":"I'm preparing a monthly inventory report. Can you take these 200 damaged stock records with handled_units and damaged_units and produce the two analytical reports — string normalization of the store groups plus a secondary-measure weighted mean — so I can see where damage is concentrated?"}],"resultDescription":"Two analytical reports: (1) string-normalised versions of the store_group and date fields across all submitted rows, and (2) a secondary-measure weighted mean of the chosen field (handled_units or damaged_units) weighted by the companion measure, computed locally without external lookups. Results cover up to 200 input rows.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, store_group, date, handled_units, damaged_units) — validation error","Invalid date format (must match YYYY-MM-DD) — schema validation failure","Null values in both handled_units and damaged_units may cause weighted mean to be undefined","Invalid enum value for field parameter — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of retail store/date labels and a weighted-mean statistic for damaged stock in a single low-cost call. It is ideal for offline batch analysis of up to 200 rows where no external lookup, prediction, or live data is required and you want two bundled reports for $0.01 USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:35:37.160Z","isFirstParty":false,"canonicalSlug":"damaged-stock-string-normalisation-and-secondary-measure-weighted-mean-226fd1cb"}