{"uid":"cap_tJ_r32Yy8FdgZAIwrnCns","slug":"pipeline-durations-iqr-outlier-flags-date-by-category-pivot-f1ded088","name":"Pipeline Durations IQR Outlier Flags + Date by Category Pivot","description":"Pipeline durations — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot for supplied pipeline durations records (run_seconds, processed_rows). 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-05-178?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","run_seconds","processed_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"run_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","run_seconds":10,"dataset_group":"group-a","processed_rows":1},{"date":"2026-10-02","row_id":"row-2","run_seconds":20,"dataset_group":"group-b","processed_rows":2},{"date":"2026-10-03","row_id":"row-3","run_seconds":15,"dataset_group":"group-a","processed_rows":3},{"date":"2026-10-04","row_id":"row-4","run_seconds":30,"dataset_group":"group-b","processed_rows":4}],"maxItems":200,"minItems":1},"field":{"enum":["run_seconds","processed_rows"],"type":"string","default":"run_seconds"}}},"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_wEdhdOlmju1V08S5SXIET","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":"Analyzes pipeline duration records to produce IQR-based outlier flags and a date-by-category pivot table for up to 200 rows.","exampleAgentPrompt":"Can you run IQR outlier detection and a date-by-category pivot on my pipeline duration data — I have about 50 rows with run_seconds and dataset_group fields, and I want to flag outliers using the run_seconds field?","exampleUseCases":[{"title":"ETL pipeline anomaly detection","prompt":"I have 120 pipeline run records with run_seconds and dataset_group — can you flag which runs are IQR outliers in run_seconds and also give me a pivot of run times by date and group?"},{"title":"Data throughput outlier review","prompt":"Run IQR outlier flagging on processed_rows for my pipeline batch records and give me a date-by-category pivot table — I've got about 80 rows with dataset_group and date columns."},{"title":"Daily pipeline performance summary","prompt":"I want to understand which of my daily pipeline runs are statistically unusual — can you detect IQR outliers in run_seconds and produce a date-by-category breakdown for these 60 pipeline records?"}],"resultDescription":"Returns two analytical reports for the supplied pipeline records: (1) IQR outlier flags identifying which rows have statistically anomalous values for the selected field (run_seconds or processed_rows), and (2) a pivot table organizing values by date and dataset_group category. No external data is fetched; all computation is local to the supplied rows.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds limit","Missing required fields (row_id, dataset_group, date, run_seconds, processed_rows) in any row","Invalid date format (must be YYYY-MM-DD) causes schema validation failure","Null values in the selected field may reduce IQR computation accuracy","Payment of 0.01 USDC not completed — request not processed"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based statistical outlier detection and a date-by-category pivot table in a single low-cost call for pipeline duration data with up to 200 rows. Ideal for data engineers and analytics agents monitoring ETL job health who want offline, reproducible statistical analysis without live external lookups or ML predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:40:01.559Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-iqr-outlier-flags-date-by-category-pivot-f1ded088"}