{"uid":"cap_IE5I4jTAJSTSPocitgFlK","slug":"damaged-stock-string-normalisation-two-measure-correlation-d7e916e9","name":"Damaged Stock String Normalisation + Two-Measure Correlation","description":"Damaged stock — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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-18-033?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}}},"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_zqEzQwwyIQMG--c_FLb1H","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/group strings and computes two-measure correlation across up to 200 damaged stock records (handled_units vs damaged_units), returning two analytical reports per call.","exampleAgentPrompt":"I have up to 200 rows of damaged stock records with handled_units and damaged_units per store group and date — can you normalise the store group strings and run a two-measure correlation between handled and damaged units, giving me both reports?","exampleUseCases":[{"title":"Warehouse damage rate correlation check","prompt":"I've got 150 rows of daily damaged stock data from our warehouse, each with a store group name, date, units handled, and units damaged — can you normalise the store group labels and run a correlation between handled and damaged units so I can see if higher throughput is driving more damage?"},{"title":"Retail chain string cleanup and damage analysis","prompt":"Our store group names in the damage log are inconsistent — things like 'Grp-A', 'group a', 'Group A' all mixed together. Can you normalise those strings and then run the two-measure correlation on the handled vs damaged units across our 80 records?"},{"title":"Monthly damaged stock audit across store groups","prompt":"Here are 200 rows of damaged stock entries from October covering store groups North and South, each with handled and damaged unit counts — please normalise the group names and produce both correlation reports so I can present them at our monthly ops review."}],"resultDescription":"Two independent analytical reports per call: (1) a string normalisation output standardising store_group labels across the submitted rows, and (2) a two-measure correlation report quantifying the statistical relationship between handled_units and damaged_units across up to 200 rows. No external lookups, predictions, or live data are included.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, handled_units, damaged_units, date, store_group) causes row rejection","Null or non-numeric values for handled_units or damaged_units may reduce correlation reliability","Dates not matching YYYY-MM-DD pattern will fail schema validation","Insufficient row variance may produce a degenerate or undefined correlation result"],"whenToPreferThis":"Choose this endpoint when you need to both clean up inconsistent store group string labels AND compute a statistical correlation between two numeric measures (handled vs damaged units) in a single low-cost call ($0.01 USDC for 2 reports). It is ideal for offline/batch retail damage audits on datasets up to 200 rows where no live lookup, prediction, or external data enrichment is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:49:53.304Z","isFirstParty":false,"canonicalSlug":"damaged-stock-string-normalisation-two-measure-correlation-d7e916e9"}