{"uid":"cap_Dy7rXAxnEtkcRjCQgp_b_","slug":"bundle-sales-string-normalisation-and-numeric-row-ordering-3c5607c1","name":"Bundle Sales String Normalisation and Numeric Row Ordering","description":"Bundle sales — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering for supplied bundle sales records (bundles_sold, component_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-15-025?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","bundles_sold","component_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},"bundles_sold":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"component_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","store_group":"group-a","bundles_sold":10,"component_units":1},{"date":"2026-10-02","row_id":"row-2","store_group":"group-b","bundles_sold":20,"component_units":2},{"date":"2026-10-03","row_id":"row-3","store_group":"group-a","bundles_sold":15,"component_units":3},{"date":"2026-10-04","row_id":"row-4","store_group":"group-b","bundles_sold":30,"component_units":4}],"maxItems":200,"minItems":1},"field":{"enum":["bundles_sold","component_units"],"type":"string","default":"bundles_sold"}}},"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_LYH1L22OfZ3lYzbprDlLr","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 orders rows by a chosen numeric field (bundles_sold or component_units) for up to 200 bundle sales records, returning 2 analytical reports per call.","exampleAgentPrompt":"Take these 4 bundle sales rows — row-1 group-a 2026-10-01 bundles_sold=10 component_units=1, row-2 group-b 2026-10-02 bundles_sold=20 component_units=2, row-3 group-a 2026-10-03 bundles_sold=15 component_units=3, row-4 group-b 2026-10-04 bundles_sold=30 component_units=4 — normalise the store_group strings and sort all rows by bundles_sold.","exampleUseCases":[{"title":"Clean retailer names and rank by volume","prompt":"I have 50 bundle sales records from different store groups with inconsistent capitalisation — can you normalise the store_group field and order all rows by bundles_sold descending so I can see top performers first?"},{"title":"Sort component unit counts across branches","prompt":"Here are 120 daily bundle sales rows across our store groups; please clean up the store_group strings and rank the rows by component_units so I can identify which days had the highest unit throughput."},{"title":"Prepare bundle data for quarterly report","prompt":"I need to clean and sort this batch of 80 bundle sales records before our quarterly review — normalise the store group names and order everything by bundles_sold so it's ready for the slide deck."}],"resultDescription":"Two analytical reports: (1) a string normalisation report showing cleaned/standardised store_group values for each row, and (2) a numeric row ordering report ranking all supplied rows by the chosen field (bundles_sold or component_units). Covers up to 200 input rows per call.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, bundles_sold, component_units) cause row-level errors","Invalid date format (not YYYY-MM-DD) causes validation failure","field enum value not one of 'bundles_sold' or 'component_units' causes parameter error","Numeric values outside ±1,000,000,000,000 range rejected"],"whenToPreferThis":"Choose this endpoint when you have up to 200 bundle sales records and need both string normalisation of store group names and numeric sorting by bundles_sold or component_units in a single low-cost call ($0.01 USDC for 2 reports). It operates entirely on supplied data with no external lookups, making it deterministic and fast. Prefer it over general-purpose data tools when your data fits the bundles_sold/component_units schema and you want both cleaning and ordering in one step.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T00:51:40.981Z","isFirstParty":false,"canonicalSlug":"bundle-sales-string-normalisation-and-numeric-row-ordering-3c5607c1"}