{"uid":"cap_tOVcCuDohcrqmPwSfdXPt","slug":"data-reconciliation-string-normalisation-iqr-outlier-flags-961b1891","name":"Data Reconciliation — String Normalisation + IQR Outlier Flags","description":"Data reconciliation — String normalisation + IQR outlier flags: String normalisation + 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-11-032?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_QS6_ARGeAzpk0RXDkqFH7","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":"Normalises string fields and flags IQR-based outliers in source/target reconciliation row data, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run string normalisation and IQR outlier detection on my reconciliation data? I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, source_totals of 10, 20, 15, 30 and target_totals of 1, 2, 3, 4 — flag outliers on the source_total field and give me both analytical reports.","exampleUseCases":[{"title":"Financial ledger reconciliation outlier check","prompt":"I have 50 rows of daily ledger reconciliation data with source_total and target_total values across two groups — 'GL' and 'SubLedger' — covering January 2025. Can you normalise the string fields and flag any IQR outliers in source_total so I can see which dates have anomalous figures?"},{"title":"ETL pipeline data quality audit","prompt":"After running our ETL job, I've got 120 rows comparing source database totals to target warehouse totals across three dataset groups. Run string normalisation and IQR-based outlier flagging on the target_total column and give me both reports so I can spot which rows need investigation."},{"title":"Monthly sales reconciliation anomaly detection","prompt":"We're reconciling monthly sales figures across regional groups for Q3 2024 — about 90 rows with source_total from our CRM and target_total from our ERP. Can you normalise the group label strings and flag statistical outliers in source_total using the IQR method?"}],"resultDescription":"Two independent analytical reports covering: (1) string normalisation results for the dataset_group and related string fields across the supplied rows, and (2) IQR-based outlier flags identifying which rows have source_total or target_total values that fall outside the interquartile range. Reports are computed locally on the supplied data with no external lookups.","failureModes":["Fewer than 1 or more than 200 rows supplied — request rejected","Missing required fields (row_id, dataset_group, date, source_total, target_total) — validation error","Date strings not matching YYYY-MM-DD format — schema validation failure","Numeric values outside ±1,000,000,000,000 range — schema rejection","Invalid field enum value (must be 'source_total' or 'target_total') — error response","Insufficient data variance for meaningful IQR computation (e.g. all identical values)"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) local statistical analysis of reconciliation row data — specifically string normalisation combined with IQR outlier detection — without requiring external data lookups, ML predictions, or professional assessments. Ideal for ETL validation, financial reconciliation audits, and data quality checks on up to 200 source/target row pairs where two bundled analytical reports are needed in a single call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:22.476Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-string-normalisation-iqr-outlier-flags-961b1891"}