{"uid":"cap_GamF5NWFJblm-LIBMBOL_","slug":"data-reconciliation-analytics-record-quality-audit-string-0292c5e3","name":"Data Reconciliation Analytics — Record Quality Audit, String Normalisation & Category Frequencies","description":"Data reconciliation — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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-11-195?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_DzKyAcS-dGM-9fpYV9Gpb","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 three local analytical reports on supplied data reconciliation rows: a record quality audit, string normalisation, and category frequency analysis — all in a single paid call.","exampleAgentPrompt":"Can you run a full quality audit, string normalisation, and category frequency report on these reconciliation records — I have rows with row_id, dataset_group, date, source_total, and target_total fields, up to 200 rows covering groups like 'group-a' and 'group-b' across dates in October 2026?","exampleUseCases":[{"title":"Monthly reconciliation quality check","prompt":"I've got about 150 reconciliation rows for October 2026 with source and target totals across dataset groups like 'retail' and 'wholesale' — can you audit the record quality, normalise the group name strings, and tell me how frequently each category appears?"},{"title":"Data pipeline validation before reporting","prompt":"Before I send this reconciliation dataset to finance, can you run a record quality audit and category frequency report on my 80 rows? Each row has a row_id, a date, a dataset_group, and source and target totals — I want to catch any nulls or malformed strings first."},{"title":"Comparing source and target totals across groups","prompt":"I have 200 reconciliation records split across 'group-a' and 'group-b' with dates from 2026-09-01 to 2026-09-30 — please run the quality audit, string normalisation, and category frequency analysis so I can see which groups have the most records and whether there are any data issues."}],"resultDescription":"Returns three analytical reports for the supplied rows: (1) a record quality audit flagging nulls, invalid formats, and completeness issues; (2) a string normalisation report standardising dataset_group and other string fields; (3) a category frequency table showing how often each dataset_group value appears. All computed locally with no external lookups.","failureModes":["More than 200 rows submitted — request rejected","Missing required fields (row_id, dataset_group, date, source_total, target_total) in one or more rows — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Numeric totals outside allowed range (±1,000,000,000,000) — rejected","Empty rows array — minItems constraint violation","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 reconciliation records with source and target totals and need a bundled analytical package covering quality auditing, string normalisation, and category frequency counting in a single low-cost call. It is ideal for data pipeline validation, pre-reporting QA, and categorical summarisation where no external data lookup or prediction is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:54.536Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-analytics-record-quality-audit-string-0292c5e3"}