{"uid":"cap_FEiMs_F0Z-zNMtQ2uqNuJ","slug":"data-reconciliation-string-normalisation-exact-row-deduplication-a158badb","name":"Data Reconciliation — String Normalisation + Exact Row Deduplication","description":"Data reconciliation — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication 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-11-020?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_oVdszbsSU3dqh0DNHdMR4","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 strings and deduplicates exact rows across supplied data reconciliation records comparing source and target totals, returning 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you reconcile these rows for me — I have source and target totals for groups A and B across dates in October 2026, up to 200 rows, and I need string normalisation plus exact row deduplication across both datasets? Here are the rows: row-1 on 2026-10-01, group-a, source 10, target 1; row-2 on 2026-10-02, group-b, source 20, target 2.","exampleUseCases":[{"title":"Financial ledger source-target reconciliation","prompt":"I have 150 rows of daily ledger entries with source totals from our ERP and target totals from the bank feed — can you run string normalisation and exact row deduplication on them so I can identify duplicates before month-end close? The rows span October 2026 across two groups: retail and wholesale."},{"title":"ETL pipeline deduplication audit","prompt":"Our ETL pipeline is producing duplicate records between the source warehouse and the target data mart. I have 80 rows with row IDs, dates from 2026-09-01 to 2026-09-30, dataset group 'orders', and both source and target totals — please run exact row dedup and string normalisation and give me the two reports."},{"title":"Supplier invoice matching cleanup","prompt":"Before we process payments, can you deduplicate and normalise these 45 supplier invoice rows? Each row has an invoice ID, a date, a supplier group, a source total from procurement, and a target total from accounts payable — I need to catch any exact duplicate rows and fix string inconsistencies."}],"resultDescription":"Returns 2 independent analytical reports: (1) a string normalisation report showing how string fields were standardised across the submitted rows, and (2) an exact row deduplication report identifying which rows are exact duplicates within the supplied dataset. Reports are computed locally with no external lookups or predictions, covering up to 200 rows of source_total / target_total reconciliation data.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, source_total, target_total) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","source_total or target_total outside ±1,000,000,000,000 range — validation error","Empty rows array (minItems: 1 not met) — validation error","row_id exceeds 80 characters — validation error"],"whenToPreferThis":"Choose this endpoint when you need to clean and deduplicate a batch of up to 200 reconciliation records comparing source and target numeric totals, especially when string normalisation is also required. It is best suited for offline, supplied-data analysis with no need for live lookups, predictions, or external data enrichment. Prefer it over general-purpose deduplication tools when your data is structured as date-grouped, keyed rows with source and target totals, such as financial ledger reconciliation or ETL validation workflows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:16.823Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-string-normalisation-exact-row-deduplication-a158badb"}