{"uid":"cap_EOXFrUR2kOy5iN82KtQvh","slug":"shipment-costs-string-normalisation-b7077e1f","name":"Shipment Costs String Normalisation","description":"Shipment costs - String normalisation: String normalisation for Shipment costs. Up to 200 supplied typed records. Local numeric and data-quality processing; no external enrichment or forecasting. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded string normalisation deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-analytics-shipment-costs-normalise?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","carrier","date","weight_kg","shipping_cost"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"carrier":{"type":"string","default":"","maxLength":80},"weight_kg":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"shipping_cost":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","carrier":"group-a","weight_kg":10,"shipping_cost":1},{"date":"2026-10-02","row_id":"row-2","carrier":"group-b","weight_kg":20,"shipping_cost":2},{"date":"2026-10-03","row_id":"row-3","carrier":"group-a","weight_kg":15,"shipping_cost":3},{"date":"2026-10-04","row_id":"row-4","carrier":"group-b","weight_kg":30,"shipping_cost":4}],"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_FFhwrq08U3aJOy_hwFkAb","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 carrier names, dates, and numeric fields in shipment cost records, returning cleaned and standardised data for up to 200 rows per call.","exampleAgentPrompt":"Can you normalise these shipment cost records for me? I have about 50 rows with carrier names like 'FedEx', 'fed-ex', and 'FEDEX', dates in YYYY-MM-DD format, weights in kg, and shipping costs in USD — I need them cleaned and standardised so the carrier strings are consistent.","exampleUseCases":[{"title":"Pre-analytics carrier name cleanup","prompt":"I've exported 120 rows of shipment data from our TMS and the carrier field is a mess — 'UPS', 'ups inc', 'U.P.S.' all appear for the same carrier. Can you normalise those carrier strings along with the dates and cost fields so I can load them into our reporting dashboard cleanly?"},{"title":"Freight cost data quality audit","prompt":"Before I hand this shipment cost dataset to the finance team, I need to clean it up — there are about 80 rows with inconsistent carrier names and some null weight and cost values. Can you run string normalisation on the carrier and date fields and flag any data quality issues?"},{"title":"Logistics pipeline data preparation","prompt":"We're ingesting daily shipment cost feeds from three carriers and need to standardise the strings before they hit our data warehouse. Can you take these 200 rows with carrier, date, weight_kg, and shipping_cost fields and normalise them so everything is in a consistent format?"}],"resultDescription":"Returns up to 200 cleaned and normalised shipment cost records with standardised carrier name strings, validated date formats, and cleaned numeric weight and cost values, along with data quality indicators per row.","failureModes":["Rows exceeding 200 items will be rejected with a validation error","Missing required fields (row_id, carrier, date, weight_kg, shipping_cost) will cause row-level errors","Dates not matching YYYY-MM-DD pattern will fail schema validation","Numeric fields outside allowed range will be rejected","Empty rows array will return a schema validation error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 shipment cost records with dirty or inconsistent carrier name strings, dates, or numeric fields that need local normalisation before downstream analytics or reporting — no external enrichment or forecasting is performed, making it suitable for purely structural data quality work on logistics cost data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T04:06:43.646Z","isFirstParty":false,"canonicalSlug":"shipment-costs-string-normalisation-b7077e1f"}