{"uid":"cap_NHw4efwEDpoaA4UCXQLLm","slug":"data-reconciliation-iqr-outlier-flags-date-by-category-pivot-916819fa","name":"Data Reconciliation — IQR Outlier Flags + Date by Category Pivot","description":"Data reconciliation — IQR outlier flags + Date by category pivot: IQR outlier flags + 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-11-178?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_-FhGpIlKPlrKv81nNh0-O","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 two local analytical reports on supplied data reconciliation records: IQR-based outlier detection and a date-by-category pivot table, for up to 200 rows.","exampleAgentPrompt":"Run IQR outlier detection and a date-by-category pivot on my reconciliation records — here are up to 200 rows with row_id, dataset_group, date, source_total, and target_total — flag any outliers in the source_total field and give me both reports.","exampleUseCases":[{"title":"ETL pipeline discrepancy audit","prompt":"I've got a batch of reconciliation records from our ETL run — each row has a date, a dataset group, and source vs target totals. Can you run IQR outlier detection on source_total and also pivot the data by date and category so I can see where the big gaps are?"},{"title":"Monthly finance close variance check","prompt":"Before we close the books this month, flag any outlier rows in our source and target reconciliation data using IQR, and give me a pivot table breaking down totals by date and dataset group so I can spot patterns."},{"title":"Data quality validation for migration","prompt":"We just migrated data between systems and I need to check for anomalies. Here are 150 rows with source_total and target_total — run the IQR outlier flags on target_total and produce a date-by-category pivot so my team can see where things look off."}],"resultDescription":"Returns two analytical reports: (1) IQR outlier flags identifying which rows have source_total or target_total values outside the interquartile range, and (2) a pivot table grouping totals by date and dataset_group category. All computation is local with no external data lookups.","failureModes":["Fewer than 1 row supplied — minimum row count not met","More than 200 rows submitted — exceeds maximum row limit","Missing required fields (row_id, dataset_group, date, source_total, target_total) on any row","Date field does not match YYYY-MM-DD format — pattern validation failure","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — schema rejection","Invalid field enum value (must be 'source_total' or 'target_total')"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based outlier detection and a date-by-category pivot in a single low-cost call on reconciliation data (source_total vs target_total). Ideal for ETL validation, financial close checks, or data migration audits where privacy matters (no external lookups). Best when you have up to 200 structured rows and want two complementary analytical views for $0.01 USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:48.842Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-iqr-outlier-flags-date-by-category-pivot-916819fa"}