{"uid":"cap_o0FCirPBl1Jc0etRvcENT","slug":"data-reconciliation-record-quality-audit-string-normalisation-exact-89010ccb","name":"Data Reconciliation — Record Quality Audit + String Normalisation + Exact Row Deduplication","description":"Data reconciliation — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication for supplied data reconciliation records (source_total, target_total). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-191?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_Z0HAke2qOnarhg0ExSUqw","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":"Audits record quality, normalises strings, and deduplicates exact rows for up to 200 data reconciliation records (source_total vs target_total), returning 3 independent analytical reports.","exampleAgentPrompt":"Can you run a full data reconciliation check on these rows — audit the record quality, normalise the strings, and deduplicate exact matches? I have up to 50 rows with row IDs, dataset groups like 'group-a' and 'group-b', dates in YYYY-MM-DD format, and source and target totals for each.","exampleUseCases":[{"title":"Finance team reconciliation pre-close audit","prompt":"Before month-end close, can you audit the quality of our reconciliation records, normalise any messy string fields, and flag exact duplicate rows? I've got about 120 rows with row IDs, dates, source totals, and target totals across groups like 'ledger-a' and 'ledger-b'."},{"title":"ETL pipeline deduplication check","prompt":"After our nightly ETL run, I need to check these 80 reconciliation records for exact duplicate rows, string inconsistencies, and overall record quality — source and target totals are included for each row, grouped by pipeline stage."},{"title":"Data migration source-target validation","prompt":"We just migrated data and I want to run a reconciliation quality audit on 200 rows comparing source_total and target_total values — can you also deduplicate any exact duplicate rows and normalise string fields across dataset groups like 'accounts-payable' and 'accounts-receivable'?"}],"resultDescription":"Returns 3 independent reports per call: (1) a record quality audit highlighting issues in the supplied rows, (2) a string normalisation report showing standardised field values, and (3) an exact row deduplication report identifying and removing duplicate records — all computed locally without external lookups or predictions.","failureModes":["More than 200 rows submitted — request rejected due to maxItems limit","Missing required fields (row_id, dataset_group, date, source_total, target_total) — validation error","Date fields not in YYYY-MM-DD format — pattern validation failure","Numeric totals outside the allowed range (±1,000,000,000,000) — schema error","Empty rows array — minItems constraint violation"],"whenToPreferThis":"Choose this endpoint when you need three simultaneous analytical reports (quality audit, string normalisation, exact deduplication) on reconciliation data in a single call for a flat fee of $0.01 USDC. It is ideal for batch reconciliation datasets up to 200 rows where no external data lookup, prediction, or professional assessment is needed — purely local analytical processing of source vs target totals.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:56.157Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-record-quality-audit-string-normalisation-exact-89010ccb"}