{"uid":"cap_A3lRHY40T4DguiDnaQQNn","slug":"numeric-precision-comparisons-string-normalisation-iqr-outlier-flags-6563352e","name":"Numeric Precision Comparisons — String Normalisation + IQR Outlier Flags","description":"Numeric precision comparisons — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied numeric precision comparisons records (source_value, transformed_value). 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-20-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["source_value","transformed_value"],"type":"string","default":"source_value"}}},"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_e7SmD4qlF449STIoVh15k","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":"Performs string normalisation and IQR-based outlier flagging on up to 200 rows of supplied numeric precision comparison records (source_value vs transformed_value), producing 2 independent analytical reports locally.","exampleAgentPrompt":"Can you run IQR outlier flagging and string normalisation on these 50 rows of numeric precision comparisons — each row has a row_id, dataset_group, date, source_value, and transformed_value — and give me both analytical reports? I want to analyse the source_value field.","exampleUseCases":[{"title":"ETL pipeline numeric anomaly audit","prompt":"I've got 120 rows from our ETL output with source_value and transformed_value columns — can you run IQR outlier detection and string normalisation on them grouped by dataset_group and date, and flag which rows look anomalous in the source_value field?"},{"title":"Data quality check before model training","prompt":"Before I pass this dataset into our model, can you check these 80 precision comparison records for outliers using IQR and normalise the strings? Each row has a row_id, date, dataset_group, source_value, and transformed_value — flag anything suspicious in transformed_value."},{"title":"Comparing two numeric measurement systems","prompt":"We have two measurement systems producing source_value and transformed_value for the same observations across 150 rows — can you run the IQR outlier flags and string normalisation reports on this data to see where the biggest discrepancies and anomalies are in the source_value field?"}],"resultDescription":"Returns 2 independent analytical reports per call: one covering string normalisation of the selected field across the supplied rows, and one providing IQR-based outlier flags identifying which rows fall outside the interquartile range. Reports are derived entirely from the supplied data using local analytical engines with no external lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, source_value, or transformed_value) — validation error","Numeric values outside allowed range (-1e12 to 1e12) — schema rejection","Invalid date format (not YYYY-MM-DD) — pattern validation failure","Empty rows array — minItems violation","Invalid field selector (not 'source_value' or 'transformed_value') — enum error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical analysis of numeric precision comparison pairs (source vs transformed values) without any external data dependencies. It is ideal for ETL validation, data quality auditing, and anomaly detection on small-to-medium datasets (up to 200 rows) where you want both IQR outlier flagging and string normalisation in a single low-cost call. Prefer alternatives if you need real-time lookups, predictive modelling, professional assessments, or datasets larger than 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:39:42.900Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-string-normalisation-iqr-outlier-flags-6563352e"}