{"uid":"cap_6xbDAxeyZUEz4dTf0cVd7","slug":"data-freshness-numeric-distribution-iqr-outlier-flags-1300817d","name":"Data Freshness — Numeric Distribution + IQR Outlier Flags","description":"Data freshness — Numeric distribution + IQR outlier flags: Numeric distribution + IQR outlier flags for supplied data freshness records (age_minutes, record_count). 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-06-080?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","age_minutes","record_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"age_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"record_count":{"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","age_minutes":10,"record_count":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","age_minutes":20,"record_count":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","age_minutes":15,"record_count":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","age_minutes":30,"record_count":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["age_minutes","record_count"],"type":"string","default":"age_minutes"}}},"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_1O8CClIWK4ekGH-ItbMUu","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 freshness records (age_minutes or record_count), returning two independent analytical reports.","exampleAgentPrompt":"Analyze the distribution and flag IQR outliers for my data freshness records — here are 5 rows with age_minutes values: row-1/group-a/2024-01-01/10/100, row-2/group-a/2024-01-02/12/95, row-3/group-b/2024-01-03/9/200, row-4/group-b/2024-01-04/180/102, row-5/group-a/2024-01-05/11/98 — run the report on the age_minutes field.","exampleUseCases":[{"title":"Detect stale pipelines in ETL monitoring","prompt":"I have 50 daily ETL pipeline freshness records with age_minutes logged per dataset group — can you compute the numeric distribution and flag any IQR outliers in the age_minutes field so I can see which pipelines are unusually stale?"},{"title":"Audit record count anomalies across groups","prompt":"Here are 120 rows of data freshness snapshots with record_count per dataset group and date — run the distribution and IQR outlier analysis on record_count so I can identify which groups have abnormally high or low counts."},{"title":"Validate data freshness before dashboard publish","prompt":"Before I publish today's data quality dashboard, can you run a numeric distribution and IQR outlier check on these 30 freshness records using the age_minutes field to make sure nothing looks off?"}],"resultDescription":"Returns two independent analytical reports per call: (1) a numeric distribution summary (e.g. min, max, mean, median, quartiles) and (2) IQR-based outlier flags for each row, covering up to 200 input rows for either the age_minutes or record_count field.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, age_minutes, record_count) cause validation errors","Invalid date format (not YYYY-MM-DD) causes schema rejection","Null values in the selected numeric field may affect distribution calculations","Invalid field enum value (not age_minutes or record_count) causes error"],"whenToPreferThis":"Choose this endpoint when you have raw data freshness records (with age_minutes and/or record_count fields) and need both a statistical distribution summary and IQR-based outlier flagging in a single call, without requiring any external data lookup or prediction. Best suited for batch analytical validation of up to 200 rows at a time within a data quality or monitoring workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T07:24:59.754Z","isFirstParty":false,"canonicalSlug":"data-freshness-numeric-distribution-iqr-outlier-flags-1300817d"}