{"uid":"cap_gKBSdnEpzbMsmh-x_XhrX","slug":"data-reconciliation-record-quality-audit-string-normalisation-5b3e0e04","name":"Data Reconciliation — Record Quality Audit + String Normalisation","description":"Data reconciliation — Record quality audit + String normalisation: Record quality audit + String normalisation 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-11-001?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}}},"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_b5-GWWe77TeBL0Qxd0zkd","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":"Runs two local analytical reports — a record quality audit and string normalisation — on up to 200 data reconciliation rows comparing source and target totals.","exampleAgentPrompt":"Can you run a record quality audit and string normalisation on these reconciliation rows I have — up to 200 rows each with a row_id, dataset_group, date, source_total, and target_total — and give me both reports?","exampleUseCases":[{"title":"Finance team source-target discrepancy audit","prompt":"I have 150 reconciliation records from our finance pipeline with source_total and target_total values by date and dataset group — can you run a record quality audit and string normalisation on them and tell me where the issues are?"},{"title":"ETL pipeline data quality check","prompt":"After our nightly ETL run I want to audit the quality of these reconciliation rows and normalise the string fields — I've got about 80 rows with row IDs, dataset groups, dates, and source vs target totals ready to send."},{"title":"Month-end close reconciliation validation","prompt":"For our month-end close, can you take these 200 reconciliation records — each has a row_id, dataset_group, a date in YYYY-MM-DD format, and source and target totals — and produce a quality audit plus string normalisation report on them?"}],"resultDescription":"Two independent analytical reports: (1) a record quality audit identifying completeness, consistency, and quality issues across the supplied reconciliation rows; and (2) a string normalisation report standardising string fields in the dataset. All analysis is performed locally with no 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 row-level rejection","Malformed date strings not matching YYYY-MM-DD pattern fail schema validation","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array is rejected; minimum 1 row required","Payment of 0.01 USDC not supplied results in 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you need two simultaneous analytical reports — a record quality audit and string normalisation — on batches of up to 200 data reconciliation rows comparing source and target totals, without requiring any external lookups, live data, or professional advisory output. Ideal for ETL validation, finance reconciliation checks, and data pipeline quality gates.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:59.406Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-record-quality-audit-string-normalisation-5b3e0e04"}