{"uid":"cap_1x928vKRrvxfGtDgaZ5Ph","slug":"record-routing-string-normalisation-explicit-missing-value-fill-fc5324e2","name":"Record Routing — String Normalisation + Explicit Missing-Value Fill","description":"Record routing — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied record routing records (accepted_records, rejected_records). 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-analytics-19-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","accepted_records","rejected_records"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"accepted_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"rejected_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","accepted_records":10,"rejected_records":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","accepted_records":20,"rejected_records":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","accepted_records":15,"rejected_records":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","accepted_records":30,"rejected_records":4}],"maxItems":200,"minItems":1},"field":{"enum":["accepted_records","rejected_records"],"type":"string","default":"accepted_records"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_EnJuHOBGoCP-bLPEcSeeB","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 string fields and fills missing values (accepted_records, rejected_records) in record routing datasets of up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Clean up my record routing dataset — I have up to 50 rows with accepted_records and rejected_records fields, some of which are null. Fill the missing accepted_records values using median imputation and normalise the string fields. Here's the data: [{\"row_id\":\"r1\",\"dataset_group\":\"group-a\",\"date\":\"2026-10-01\",\"accepted_records\":null,\"rejected_records\":2}].","exampleUseCases":[{"title":"ETL pipeline data quality cleanup","prompt":"I have a batch of 80 record routing rows from our ETL pipeline — some accepted_records values are null. Can you normalise the string fields and fill the missing accepted_records using median imputation? Here are the rows."},{"title":"Routing rejection rate analysis prep","prompt":"Before I analyse our routing rejection rates, I need to fill any null rejected_records values using zero-fill and normalise the dataset_group strings across these 120 routing records I'm sending you."},{"title":"Daily routing report data hygiene","prompt":"I'm generating a daily routing report and my dataset has some missing values in both accepted_records and rejected_records. Please normalise all string fields and fill nulls in the rejected_records column using mean imputation across these 30 rows."}],"resultDescription":"Two independent analytical reports covering string normalisation results and missing-value fill results for the accepted_records and rejected_records fields respectively, applied to up to 200 submitted rows. No external lookups or predictions are made — all processing is local.","failureModes":["More than 200 rows submitted — request rejected","Missing required fields (row_id, date, dataset_group) — validation error","Invalid date format (must be YYYY-MM-DD) — schema validation failure","Unsupported imputation method outside enum (zero, mean, median) — rejected","Field selector outside allowed enum (accepted_records, rejected_records) — error","Numeric values outside allowed range (±1,000,000,000,000) — validation error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline string normalisation and missing-value imputation for record routing datasets (accepted/rejected record counts) without any external data lookups, predictions, or live API calls. Ideal for ETL preprocessing, data quality checks, and pipeline hygiene steps on batches up to 200 rows at minimal cost ($0.01 USDC for two reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:18.963Z","isFirstParty":false,"canonicalSlug":"record-routing-string-normalisation-explicit-missing-value-fill-fc5324e2"}