{"uid":"cap_GXNhJvH4IivYZcl2LFcY9","slug":"pipeline-durations-numeric-interval-filter-iqr-outlier-flags-8fca482e","name":"Pipeline Durations – Numeric Interval Filter + IQR Outlier Flags","description":"Pipeline durations — Numeric interval filter + IQR outlier flags: Numeric interval filter + IQR outlier flags 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-05-130?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_GlkAJr_T030Et2qVfZ2ZN","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":"Filters pipeline duration records by a numeric interval and flags IQR-based outliers across run_seconds and processed_rows fields, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you analyze these 50 pipeline run records and flag any IQR outliers in run_seconds, filtering to only rows where run_seconds is between 5 and 300? Here's the data with row_id, date, dataset_group, run_seconds, and processed_rows for each run.","exampleUseCases":[{"title":"ETL pipeline anomaly detection","prompt":"I have 120 pipeline run records with run_seconds and processed_rows — can you filter them to runs where run_seconds is between 10 and 600 and flag any IQR outliers in that field so I know which jobs are abnormally slow or fast?"},{"title":"Data volume outlier flagging","prompt":"Here are 80 daily pipeline records — please apply an interval filter from 0 to 5000000 on the processed_rows field and flag any rows that are IQR outliers, so I can spot days where an unusually large or small batch was processed."},{"title":"Multi-group pipeline QA report","prompt":"I've got pipeline logs for two dataset groups across the past quarter, about 150 rows total. Can you run the numeric interval filter from 0 to 1800 on run_seconds and generate the IQR outlier report so I can see which runs fall outside normal bounds?"}],"resultDescription":"Returns two analytical reports: (1) records filtered to those whose selected field (run_seconds or processed_rows) falls within the specified [minimum, maximum] interval, and (2) IQR-based outlier flags indicating which records fall outside the interquartile fences — all computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row submitted returns a validation error","More than 200 rows submitted exceeds the limit and is rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Missing required fields (row_id, run_seconds, processed_rows) cause rejection","Minimum greater than maximum produces an empty or ill-defined interval result","All-null numeric field values may result in degenerate IQR computation"],"whenToPreferThis":"Choose this endpoint when you have up to 200 pipeline execution records with run_seconds and/or processed_rows fields and need both a numeric range filter and IQR-based outlier detection in a single paid call. It is best suited for offline batch analysis of historical pipeline logs rather than real-time monitoring, and is cost-effective at $0.01 USDC for two reports simultaneously.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:12.971Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-numeric-interval-filter-iqr-outlier-flags-8fca482e"}