{"uid":"cap_GlU6hsjDVpLNxTHcN-XIT","slug":"data-reconciliation-base-100-series-iqr-outlier-flags-05ecf9ee","name":"Data Reconciliation — Base-100 Series + IQR Outlier Flags","description":"Data reconciliation — Base-100 series + IQR outlier flags: Base-100 series + 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-11-164?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"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_TdlysieZT6gIBEcMFrQgU","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 a base-100 indexed time series and IQR-based outlier flags for up to 200 source/target reconciliation rows, returning two analytical reports in one call.","exampleAgentPrompt":"I have 4 rows of reconciliation data — row-1 through row-4 across group-a and group-b from 2026-10-01 to 2026-10-04 with source_totals of 10, 20, 15, 30 and target_totals of 1, 2, 3, 4 — can you run the base-100 series and IQR outlier detection on the source_total field, using row-1 (index 0) as the base?","exampleUseCases":[{"title":"Monthly finance reconciliation outlier review","prompt":"I have 12 months of source and target revenue totals for our finance team — can you index them to base-100 starting from January (index 0) and flag any months where the source_total looks like a statistical outlier based on IQR?"},{"title":"Multi-group ETL pipeline data quality check","prompt":"We ran an ETL job and I have 50 rows of source vs target counts across 5 dataset groups with dates from 2025-01-01 to 2025-02-19 — please compute the base-100 series and IQR outlier flags on the target_total field so I can spot pipeline anomalies."},{"title":"Retail inventory source-target discrepancy analysis","prompt":"I've got 30 rows of inventory reconciliation records across two store groups from Q1 2025, each with a source_total and target_total — index the source_total to base-100 from the first row and flag any IQR outliers so I know which dates had unusual discrepancies."}],"resultDescription":"Returns two analytical reports: (1) a base-100 normalized index series for the chosen field (source_total or target_total) relative to the specified base row, and (2) IQR-based outlier flags for each row indicating which records fall outside the interquartile range — all computed locally from the supplied rows with no external lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, dataset_group, source_total, target_total) — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","base_index out of range for the number of rows supplied — error response","All null values in selected field — unable to compute meaningful index or IQR","Malformed JSON payload — HTTP 400 or parse error"],"whenToPreferThis":"Choose this endpoint when you need both a normalized base-100 time series and IQR outlier detection on reconciliation data in a single low-cost call ($0.01 USDC for both reports). It is ideal for batch analytical tasks on up to 200 pre-aggregated source/target rows where no live data lookup, prediction, or external enrichment is needed — just local statistical computation on data you already have.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:55:24.904Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-base-100-series-iqr-outlier-flags-05ecf9ee"}