{"uid":"cap_966iiOcMzOIpyn7k5dcnO","slug":"data-freshness-iqr-outlier-flags-date-by-category-pivot-230d3d54","name":"Data Freshness IQR Outlier Flags + Date-by-Category Pivot","description":"Data freshness — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot 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-06-178?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_STpH7U1UZYizQKHUDUKWN","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":"Runs two local analytical reports on data freshness records: IQR-based outlier detection and a date-by-category pivot table, for up to 200 rows of age_minutes or record_count data.","exampleAgentPrompt":"Can you flag IQR outliers and generate a date-by-category pivot for my data freshness records? I have rows with row_id, dataset_group, date, age_minutes, and record_count — I want to analyze the age_minutes field across up to 200 rows.","exampleUseCases":[{"title":"Detect stale pipeline records","prompt":"I have a list of dataset freshness logs with age_minutes and record_counts grouped by dataset_group and date — can you flag any IQR outliers in age_minutes and give me a pivot of values by date and category?"},{"title":"ETL quality audit by group","prompt":"Run an IQR outlier check and a date-by-category pivot on my ETL freshness data — I want to spot which dataset groups have anomalous record_count values, I have about 150 rows ready to submit."},{"title":"Monitor data ingestion anomalies","prompt":"I'm monitoring my data ingestion pipeline and want to analyze record_count freshness records — can you flag statistical outliers using IQR and pivot the data by date and dataset group so I can see patterns?"}],"resultDescription":"Returns two analytical reports: (1) IQR outlier flags indicating which rows have age_minutes or record_count values outside the interquartile range fence, and (2) a pivot table grouping values by date and dataset_group category — all computed locally without any external lookup or live data access.","failureModes":["Fewer than 1 row submitted — API rejects with validation error","More than 200 rows submitted — request rejected at maxItems limit","Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — row-level validation error","Invalid date format (not YYYY-MM-DD) — pattern validation failure","Field selector not one of 'age_minutes' or 'record_count' — enum validation error","All values null for selected field — IQR computation may return undefined outlier bounds"],"whenToPreferThis":"Choose this endpoint when you need fast, local statistical analysis of data freshness metrics — specifically IQR-based outlier detection and date-category pivoting — without requiring any external data source, live lookup, or prediction. Ideal for ETL pipeline audits, data quality monitoring dashboards, or any scenario where you have up to 200 rows of age_minutes/record_count records and need two complementary analytical views in a single low-cost call ($0.01 USDC).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T06:50:57.803Z","isFirstParty":false,"canonicalSlug":"data-freshness-iqr-outlier-flags-date-by-category-pivot-230d3d54"}