{"uid":"cap_0hhAbN1zl7MqqzUJYmijR","slug":"pipeline-durations-numeric-distribution-iqr-outlier-flags-6a20d2e4","name":"Pipeline Durations — Numeric Distribution + IQR Outlier Flags","description":"Pipeline durations — Numeric distribution + IQR outlier flags: Numeric distribution + 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-05-080?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_O7pqENQnvuIDusvazt5Xp","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 pipeline duration data (run_seconds or processed_rows) across up to 200 supplied rows.","exampleAgentPrompt":"I've got 150 rows of pipeline run data with run_seconds and processed_rows — can you compute the numeric distribution and flag IQR outliers for run_seconds so I can see which runs were unusually slow?","exampleUseCases":[{"title":"ETL pipeline anomaly detection","prompt":"I have logs from our nightly ETL jobs — 120 rows with row_id, date, dataset_group, run_seconds, and processed_rows. Can you analyze the numeric distribution of run_seconds and flag any IQR outliers so I know which jobs ran abnormally long?"},{"title":"Data ingestion throughput analysis","prompt":"Here are 80 pipeline run records from last month. Can you generate a distribution report for processed_rows and flag the outliers using IQR so I can see which runs processed far more or fewer rows than typical?"},{"title":"Multi-group pipeline performance review","prompt":"I have pipeline data across two dataset groups — group-a and group-b — with about 90 rows total. Can you compute the numeric distribution and IQR outlier flags for run_seconds so I can compare which group has more variability and flag the problematic runs?"}],"resultDescription":"Returns two reports: (1) a numeric distribution summary (e.g. min, max, mean, median, quartiles, std dev) and (2) IQR-based outlier flags identifying which rows fall outside the expected range — both computed locally on the supplied data for either the run_seconds or processed_rows field.","failureModes":["Fewer than 1 row supplied — schema minimum not met","More than 200 rows submitted — exceeds maxItems limit and request is rejected","Invalid date format (not YYYY-MM-DD) — pattern validation failure","Missing required fields (row_id, dataset_group, date, run_seconds, processed_rows) — schema validation error","All values null for selected field — distribution computation may be undefined or empty","field enum value not one of run_seconds or processed_rows — rejected by schema","Payment not sent or insufficient USDC — x402 payment failure"],"whenToPreferThis":"Choose this endpoint when you have raw pipeline duration data (up to 200 rows) and need both a distribution summary and IQR outlier detection in a single call, without needing live data lookup, predictions, or external integrations. It is ideal for local, self-contained statistical analysis of ETL or batch job telemetry. Prefer this over general-purpose analytics tools when you want a cheap, deterministic, offline computation with no side effects.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:20.666Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-numeric-distribution-iqr-outlier-flags-6a20d2e4"}