{"uid":"cap_xGluR_xIg88vftftL-HJw","slug":"damaged-stock-string-normalisation-and-numeric-row-ordering-dfb77d7d","name":"Damaged Stock String Normalisation and Numeric Row Ordering","description":"Damaged stock — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering 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-18-025?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_E0-rIbXPJOlX4s2UvNpDr","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 orders rows numerically for up to 200 damaged stock records (handled_units, damaged_units), producing 2 local analytical reports per call.","exampleAgentPrompt":"I have 4 damaged stock records across two store groups — row-1 group-a 2026-10-01 handled_units=10 damaged_units=1, row-2 group-b 2026-10-02 handled_units=20 damaged_units=2, row-3 group-a 2026-10-03 handled_units=15 damaged_units=3, row-4 group-b 2026-10-04 handled_units=30 damaged_units=4 — can you run string normalisation and numeric row ordering on these and give me both the handled_units and damaged_units reports?","exampleUseCases":[{"title":"Weekly retail damage audit cleanup","prompt":"Here are 50 damaged stock rows from last week across our store groups — dates are in YYYY-MM-DD format, store names are messy. Can you normalise the strings and sort them by damaged_units so I can see the worst-hit rows first?"},{"title":"Handled vs damaged comparison report","prompt":"I've got 120 inventory records with handled_units and damaged_units values. Run both the handled_units and damaged_units reports with string normalisation and numeric ordering so I can compare damage rates across store groups."},{"title":"Small batch stock damage tidy-up","prompt":"I have 8 damaged stock rows from our group-a and group-b stores this month — row IDs row-1 through row-8, dates from 2026-10-01 to 2026-10-08 — normalise the strings and order them numerically by handled_units please."}],"resultDescription":"Returns 2 analytical reports on the supplied damaged stock rows: one covering the selected numeric field (handled_units or damaged_units) with numeric row ordering applied, and one companion report — both derived entirely from the submitted data using local analytical engines, with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request exceeds maxItems limit","Missing required fields (row_id, store_group, date, handled_units, or damaged_units) — validation error","Invalid date format not matching YYYY-MM-DD pattern — schema rejection","Numeric values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) — validation error","Invalid field enum value (must be 'handled_units' or 'damaged_units') — schema rejection","Payment not completed — x402 payment required before processing"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local string normalisation and numeric ordering on damaged stock data (up to 200 rows) without any external API calls, predictions, or professional assessments. It is ideal for retail inventory data cleaning pipelines where you want two standardised analytical reports per batch at minimal cost ($0.01 USDC). Prefer alternatives if you need live inventory lookups, damage predictions, or datasets larger than 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-31T00:43:27.636Z","isFirstParty":false,"canonicalSlug":"damaged-stock-string-normalisation-and-numeric-row-ordering-dfb77d7d"}