{"uid":"cap_vKc7ym0eb7IIYxQBtOqgB","slug":"damaged-stock-quality-audit-string-normalisation-two-measure-387c3719","name":"Damaged Stock Quality Audit + String Normalisation + Two-Measure Correlation","description":"Damaged stock — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation for supplied damaged stock records (handled_units, damaged_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-18-204?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_1GLpZowSVU3znXwuPuYbb","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 three local analytical reports on damaged stock data: record quality audit, string normalisation, and two-measure correlation between handled_units and damaged_units (up to 200 rows).","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and correlation analysis on this batch of damaged stock records? I have up to 50 rows with fields for row_id, store_group, date, handled_units, and damaged_units — I want to see all three reports bundled together.","exampleUseCases":[{"title":"Retail damage data QA pipeline","prompt":"I've got 120 rows of daily damaged stock data from our warehouse — each row has a store group, date, how many units were handled, and how many were damaged. Can you audit the record quality, normalise the store group strings, and check the correlation between handled and damaged units for me?"},{"title":"Detecting dirty store group labels","prompt":"Our damage tracking spreadsheet has inconsistent store group names — things like 'Group A', 'group-a', 'GROUP A' mixed together. Can you run string normalisation plus a quality audit on the 80 rows I'm sending so I can see what needs cleaning?"},{"title":"Validating damaged units correlation before reporting","prompt":"Before I include this in our monthly retail report, can you check whether handled_units and damaged_units are correlated in this dataset of 200 rows, and flag any quality issues with the records at the same time?"}],"resultDescription":"Three bundled analytical reports: (1) a record quality audit flagging null values, out-of-range numbers, and malformed dates across up to 200 rows; (2) a string normalisation report standardising store_group labels; (3) a two-measure correlation report quantifying the statistical relationship between handled_units and damaged_units. All computed locally with no external lookups.","failureModes":["Payload exceeds 200 rows — request rejected with validation error","Missing required fields (row_id, date, handled_units, damaged_units) cause row-level audit failures","Malformed date strings not matching YYYY-MM-DD pattern flagged or rejected","Null numeric values for both measures may degrade correlation computation","Payment not sent or insufficient USDC — 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you need three standard data-quality and analytical reports on damaged stock data in a single $0.01 call, without needing live inventory lookups, predictive modelling, or external integrations. It is ideal for offline batch QA on up to 200 rows of retail damage tracking data where record auditing, string cleaning, and correlation are all required together.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T12:56:47.367Z","isFirstParty":false,"canonicalSlug":"damaged-stock-quality-audit-string-normalisation-two-measure-387c3719"}