{"uid":"cap_T0LgVWLnipJ3oeftXgwRq","slug":"data-reconciliation-string-normalisation-adjacent-row-changes-7a30cbc1","name":"Data Reconciliation — String Normalisation + Adjacent Row Changes","description":"Data reconciliation — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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-11-029?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"}}},"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_LiSwmwL-A_Nd36Y52aAyu","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":"Analyzes up to 200 data reconciliation rows to produce string normalization and adjacent row change reports on source/target totals.","exampleAgentPrompt":"Can you run string normalization and adjacent row change analysis on my reconciliation data? I have 4 rows across groups 'group-a' and 'group-b' from 2026-10-01 to 2026-10-04, with source totals 10, 20, 15, 30 and target totals 1, 2, 3, 4 — analyze the source_total field.","exampleUseCases":[{"title":"Daily finance reconciliation audit","prompt":"I have daily source and target financial totals for two datasets from last week — can you normalize the strings and show me how each row's values changed compared to the adjacent row? Here are 7 rows with row IDs, dates from 2025-06-01 to 2025-06-07, group 'finance-ledger', and I want to analyze source_total."},{"title":"ETL pipeline discrepancy check","prompt":"After our nightly ETL run I have 50 reconciliation rows across dataset groups 'raw' and 'processed' with dates from 2025-07-01 to 2025-07-20 — run the string normalization and adjacent row change reports on the target_total field so I can spot where values shifted unexpectedly."},{"title":"Month-end data quality review","prompt":"For our month-end close, I need to check 100 reconciliation records across groups 'EMEA', 'APAC', and 'AMER' covering 2025-05-01 to 2025-05-31. Can you normalize the data and flag adjacent row changes on source_total so I can identify anomalies before signing off?"}],"resultDescription":"Returns two independent analytical reports: (1) a string normalization report for the specified field across all supplied rows, and (2) an adjacent row changes report showing sequential differences between consecutive rows, all computed locally without external data lookup.","failureModes":["Rows array empty or missing — minItems:1 not met","More than 200 rows submitted — maxItems:200 exceeded","Invalid date format not matching YYYY-MM-DD pattern","source_total or target_total outside ±1,000,000,000,000 range","Missing required fields (row_id, dataset_group, date) on any row","Invalid field enum value — must be 'source_total' or 'target_total'","Payment failure or insufficient USDC balance"],"whenToPreferThis":"Choose this endpoint when you need two bundled local analytics reports — string normalization and adjacent row change detection — on a reconciliation dataset of up to 200 rows with source and target totals, without requiring any live data lookups or external enrichment. It is cost-effective at $0.01 USDC for both reports and is ideal for ETL validation, finance close processes, or data quality audits where you already have the source and target figures in hand.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:00.011Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-string-normalisation-adjacent-row-changes-7a30cbc1"}