{"uid":"cap_Ftd55_hNpHMln_FOK4pCu","slug":"pipeline-durations-grouped-numeric-summary-with-iqr-outlier-flags-8862e4ec","name":"Pipeline Durations Grouped Numeric Summary with IQR Outlier Flags","description":"Pipeline durations — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + 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-05-094?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_JNvPv2P7PcpVHSlpukjMC","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 grouped descriptive statistics and IQR-based outlier flags for pipeline duration records (run_seconds, processed_rows) across dataset groups.","exampleAgentPrompt":"Can you analyze these pipeline run records and give me a grouped numeric summary with IQR outlier flags for the run_seconds field? I have 4 rows: row-1 on 2026-10-01 in group-a ran 10 seconds processing 1 row, row-2 on 2026-10-02 in group-b ran 20 seconds processing 2 rows, row-3 on 2026-10-03 in group-a ran 15 seconds processing 3 rows, and row-4 on 2026-10-04 in group-b ran 30 seconds processing 4 rows.","exampleUseCases":[{"title":"Detecting slow ETL job outliers","prompt":"I have 50 daily ETL pipeline runs across three dataset groups — can you group them and flag any run_seconds values that look like outliers using IQR? Here are the records with row IDs, dates, group names, run times, and processed row counts."},{"title":"Monitoring batch pipeline performance","prompt":"Every night our batch jobs run across groups like 'sales-data' and 'inventory-data' — can you compute a grouped numeric summary for processed_rows across these groups and flag any records that fall outside the IQR bounds so I know which jobs processed an unusual number of rows?"},{"title":"QA report on pipeline duration variance","prompt":"We're doing a QA review of last month's pipeline executions. Can you take these 120 pipeline duration records and give me a grouped statistical summary of run_seconds by dataset group, including IQR outlier flags so we can spot which runs were abnormally long or short?"}],"resultDescription":"Returns two analytical reports: (1) a grouped numeric summary (e.g. count, mean, median, min, max, std dev) of the chosen metric (run_seconds or processed_rows) broken down by dataset_group, and (2) IQR-based outlier flags per row indicating which records fall outside the interquartile range. Up to 200 rows supported per call.","failureModes":["More than 200 rows submitted — request exceeds row limit","Missing required fields (row_id, dataset_group, date, run_seconds, processed_rows) — validation error","Invalid date format (must be YYYY-MM-DD) — schema validation failure","Invalid field enum value (must be 'run_seconds' or 'processed_rows') — rejected request","All null values for selected metric — no computable statistics","Payment not received — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have pipeline execution records with group labels and need both descriptive group-level statistics and IQR-based outlier detection in a single call, without needing external data lookups or ML-based anomaly detection. Ideal for data engineers auditing ETL job performance, batch pipeline QA, or monitoring run time variance across dataset groups with up to 200 records.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:10.597Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-grouped-numeric-summary-with-iqr-outlier-flags-8862e4ec"}