{"uid":"cap_JPuNU4EyNfyvJnuDcpWQ9","slug":"data-reconciliation-analytics-numeric-distribution-date-by-category-a42788ce","name":"Data Reconciliation Analytics: Numeric Distribution + Date by Category Pivot","description":"Data reconciliation — Numeric distribution + Date by category pivot: Numeric distribution + Date by category pivot 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-11-083?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_dA28n-O5MQvo_Yz2JqJyC","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":"Generates two analytical reports — a numeric distribution and a date-by-category pivot — for data reconciliation records comparing source and target totals across up to 200 rows.","exampleAgentPrompt":"Can you run a numeric distribution and date-by-category pivot on these reconciliation records? I have 4 rows: row-1 on 2026-10-01 in group-a with source_total 10 and target_total 1, row-2 on 2026-10-02 in group-b with source_total 20 and target_total 2, row-3 on 2026-10-03 in group-a with source_total 15 and target_total 3, and row-4 on 2026-10-04 in group-b with source_total 30 and target_total 4 — analyze the source_total field.","exampleUseCases":[{"title":"ETL pipeline reconciliation audit","prompt":"I have 50 reconciliation records from last month's ETL run with source_total and target_total values grouped by pipeline stage and dated daily — can you give me a numeric distribution and a date-by-category pivot on the source_total so I can see where the biggest gaps are?"},{"title":"Finance month-end close discrepancy review","prompt":"Here are 120 rows of month-end reconciliation data across three ledger groups with dates in October 2026 and source and target totals for each entry — run the numeric distribution and date-by-category pivot on target_total so I can identify which groups and dates have the most variance."},{"title":"Data migration validation summary","prompt":"We just migrated data across two systems and I have 80 reconciliation rows with source_total and target_total per batch group and migration date — can you analyze the source_total field and give me both the numeric distribution and the date-by-category breakdown to validate the migration?"}],"resultDescription":"Returns two analytical reports for the supplied reconciliation records: (1) a numeric distribution of the selected field (source_total or target_total) showing statistical spread such as min, max, mean, percentiles, and bin counts; and (2) a date-by-category pivot table cross-tabulating totals by date and dataset_group. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Missing required fields (row_id, dataset_group, date, source_total, target_total) in any row — validation error","Date values not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range of -1,000,000,000,000 to 1,000,000,000,000 — schema rejection","Invalid field selector (not 'source_total' or 'target_total') — enum validation error","Payment not received or insufficient USDC — request not processed"],"whenToPreferThis":"Choose this endpoint when you need quick, local analytical computation on reconciliation data comparing source and target totals across categorical groups and dates, without sending data to an external analytics platform. It is ideal when you have up to 200 rows of structured reconciliation records and want both a numeric distribution and a pivot in a single low-cost call ($0.01 USDC for both reports). Prefer this over general-purpose analytics APIs when your data is already structured per the reconciliation schema and you need fast, offline-style analysis.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:26.949Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-analytics-numeric-distribution-date-by-category-a42788ce"}