{"uid":"cap_2SivFEhTYFgkP4lPztxDh","slug":"data-reconciliation-numeric-distribution-iqr-outlier-flags-4188380c","name":"Data Reconciliation — Numeric Distribution + IQR Outlier Flags","description":"Data reconciliation — Numeric distribution + IQR outlier flags: Numeric distribution + IQR outlier flags 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-11-080?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_Jpyd4qWBf_xsjevTuOnKF","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":"Computes numeric distribution statistics and IQR-based outlier flags for up to 200 data reconciliation records containing source and target totals.","exampleAgentPrompt":"I have 150 reconciliation rows with source_total and target_total values — can you run a numeric distribution analysis and IQR outlier detection on the source_total field and tell me which rows look statistically anomalous?","exampleUseCases":[{"title":"ETL pipeline discrepancy investigation","prompt":"I've got 80 rows of daily ETL reconciliation data with source_total and target_total amounts — run a numeric distribution and IQR outlier analysis on source_total so I can see which days had abnormal figures."},{"title":"Finance team month-end reconciliation audit","prompt":"Our finance team has 120 reconciliation records across two dataset groups for October — can you analyze the target_total field for numeric distribution stats and flag any IQR outliers so we know where to investigate?"},{"title":"Data quality check before reporting","prompt":"Before we publish our quarterly report I need to validate 60 reconciliation rows — please compute the numeric distribution and IQR outlier flags for both source_total and target_total so we can catch any bad data points."}],"resultDescription":"Two independent analytical reports covering: numeric distribution statistics (min, max, mean, median, quartiles, std dev) and IQR-based outlier flags identifying which rows fall outside the interquartile fences, for the selected field across all submitted reconciliation rows (up to 200).","failureModes":["Fewer than 1 or more than 200 rows submitted — validation error","Missing required fields (row_id, dataset_group, date, source_total, target_total) — schema rejection","Date strings not matching YYYY-MM-DD pattern — validation error","Numeric values outside ±1,000,000,000,000 range — schema rejection","Invalid field enum value (not source_total or target_total) — validation error","All source_total or target_total values are null — insufficient data for distribution computation"],"whenToPreferThis":"Choose this endpoint when you have tabular reconciliation data with source and target numeric totals and need both distribution statistics and IQR-based outlier detection in a single call without any external data lookup, live market data, or prediction. Ideal for ETL validation, financial reconciliation audits, and data quality checks on datasets up to 200 rows. The flat $0.01 USDC fee covers two reports (distribution + outlier flags) simultaneously, making it cost-efficient for paired analytical needs.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:45:25.786Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-numeric-distribution-iqr-outlier-flags-4188380c"}