{"uid":"cap_agD07-bC-NBCrmV8vMOW9","slug":"data-reconciliation-string-normalisation-explicit-missing-value-fill-c559a2f6","name":"Data Reconciliation: String Normalisation + Explicit Missing-Value Fill","description":"Data reconciliation — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied data reconciliation records (source_total, target_total). 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-11-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_total","target_total"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_total":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"target_total":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_total":10,"target_total":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_total":20,"target_total":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_total":15,"target_total":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_total":30,"target_total":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["source_total","target_total"],"type":"string","default":"source_total"},"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__yZxqcNjQc9NTIk-qKiEq","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 (via zero, mean, or median) in data reconciliation records comparing source_total vs target_total, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a batch of reconciliation rows with some null source_total and target_total values — can you fill the missing source_total values using the median and also normalise all the string fields? Here are my rows: [{\"row_id\":\"r1\",\"dataset_group\":\"sales\",\"date\":\"2025-01-15\",\"source_total\":null,\"target_total\":500},{\"row_id\":\"r2\",\"dataset_group\":\"sales\",\"date\":\"2025-01-16\",\"source_total\":300,\"target_total\":null}].","exampleUseCases":[{"title":"Fix null totals in daily sales reconciliation","prompt":"I have 80 daily reconciliation rows for my sales dataset and several source_total values are null — please fill them using the median and normalise all the string fields like dataset_group and row_id."},{"title":"Zero-fill missing target values before reporting","prompt":"Before I run my monthly close report, I need all null target_total entries in my reconciliation file replaced with zero and the string fields normalised — can you process these 150 rows for me?"},{"title":"Mean imputation for multi-group reconciliation audit","prompt":"I'm auditing reconciliation records across three dataset groups and some rows are missing source_total — fill those gaps using the mean and clean up the string fields across all 200 rows."}],"resultDescription":"Returns two independent analytical reports covering (1) string-normalised versions of the input reconciliation records and (2) the specified numeric field (source_total or target_total) with missing values explicitly filled using the chosen method (zero, mean, or median), applied locally without any external lookups or predictions.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, source_total, target_total) causes rejection","Invalid date format (not YYYY-MM-DD) fails schema validation","Numeric values outside ±1,000,000,000,000 are rejected","Invalid method enum value (not zero/mean/median) causes an error","Invalid field enum value (not source_total/target_total) causes an error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 data reconciliation rows with null or inconsistent source_total/target_total values and need both string normalisation and missing-value imputation in a single call. Ideal for ETL pipelines, financial close processes, or audit workflows where you need lightweight, deterministic local cleaning without any external data lookups or ML predictions. Not suitable for datasets larger than 200 rows, live data enrichment, or prediction tasks.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:17.599Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-string-normalisation-explicit-missing-value-fill-c559a2f6"}