{"uid":"cap_jTIoNBZA7CYv5leTMsH6p","slug":"numeric-precision-comparisons-row-ordering-iqr-outlier-flags-75f60097","name":"Numeric Precision Comparisons — Row Ordering & IQR Outlier Flags","description":"Numeric precision comparisons — Numeric row ordering + IQR outlier flags: Numeric row ordering + 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-20-119?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_I7t8iSvW34x7yooM6Oo4S","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":"Ranks numeric rows by value and flags IQR-based outliers for supplied source_value/transformed_value pairs, producing two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you rank these rows by source_value and flag any IQR outliers? I have 4 rows: row-1 (group-a, 2026-10-01, source=10, transformed=1), row-2 (group-b, 2026-10-02, source=20, transformed=2), row-3 (group-a, 2026-10-03, source=15, transformed=3), row-4 (group-b, 2026-10-04, source=30, transformed=4) — give me the numeric ordering and outlier flags report.","exampleUseCases":[{"title":"Outlier detection in sales figures","prompt":"I have a list of up to 50 daily sales records with original and adjusted values — can you rank them by source_value and flag any rows that fall outside the IQR as outliers? Here are the rows: row-1 (group-a, 2026-11-01, source=500, transformed=480), row-2 (group-b, 2026-11-02, source=12000, transformed=11800), row-3 (group-a, 2026-11-03, source=510, transformed=490)."},{"title":"Data pipeline transformation QA","prompt":"We ran a numeric transformation on our ETL pipeline and I want to verify the output — please sort these rows by transformed_value and flag any IQR outliers across these 10 records: row-1 (pipeline-A, 2026-09-01, source=100, transformed=95), row-2 (pipeline-A, 2026-09-02, source=102, transformed=5000), row-3 (pipeline-B, 2026-09-03, source=98, transformed=97)."},{"title":"Sensor reading anomaly detection","prompt":"I've got temperature sensor readings with raw and calibrated values for 20 devices — rank them by source_value and show me which ones are statistical outliers using IQR: sensor-1 (zone-north, 2026-08-15, source=22.1, transformed=21.9), sensor-2 (zone-south, 2026-08-15, source=95.3, transformed=94.8), sensor-3 (zone-north, 2026-08-16, source=21.8, transformed=21.7)."}],"resultDescription":"Two independent analytical reports: (1) all submitted rows ordered by the selected numeric field (source_value or transformed_value), and (2) a per-row IQR outlier flag indicating whether each row's value falls outside the interquartile range. Up to 200 rows processed per call.","failureModes":["Fewer than 1 row submitted — minimum 1 row required","More than 200 rows submitted — exceeds maxItems limit","Invalid date format — must match YYYY-MM-DD pattern","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected","Missing required fields (row_id, dataset_group, date) cause validation errors","Invalid field selector — must be 'source_value' or 'transformed_value'","All null values for selected field may produce empty or degenerate IQR output"],"whenToPreferThis":"Choose this endpoint when you need both numeric row ordering and IQR-based outlier detection on supplied data in a single call, without requiring an external data source or live lookup. It is ideal for data quality checks, ETL validation, or anomaly flagging on small-to-medium datasets (up to 200 rows) where you provide the source and transformed numeric values directly.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:27.946Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-row-ordering-iqr-outlier-flags-75f60097"}