{"uid":"cap_6lXFN-iAIdx5Hh_G-3d7d","slug":"pipeline-durations-adjacent-row-changes-iqr-outlier-flags-90e6d091","name":"Pipeline Durations — Adjacent Row Changes & IQR Outlier Flags","description":"Pipeline durations — Adjacent row changes + IQR outlier flags: Adjacent row changes + 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-05-157?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_MzU8qppXXR5FHVm-9VqYG","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 supplied pipeline duration records to compute adjacent row-to-row changes and flag IQR-based outliers for run_seconds or processed_rows.","exampleAgentPrompt":"Can you analyze these pipeline run records for adjacent row-to-row changes and flag any IQR outliers in the run_seconds field? I have up to 50 rows with date, row_id, dataset_group, run_seconds, and processed_rows.","exampleUseCases":[{"title":"ETL job runtime spike detection","prompt":"I have 80 rows of daily ETL pipeline run logs with run_seconds and processed_rows — can you check for any sudden jumps between consecutive runs and flag statistical outliers using IQR on the run_seconds field?"},{"title":"Data pipeline throughput anomaly review","prompt":"Here are 120 pipeline execution records grouped by dataset_group. Can you analyze adjacent row changes and IQR outlier flags on the processed_rows field so I can see which runs processed an unusual number of rows?"},{"title":"Pipeline performance regression check","prompt":"We pushed a new pipeline version last week. I have before-and-after run records — can you compute the adjacent changes and IQR-based outlier flags on run_seconds so I can tell if runtimes got worse?"}],"resultDescription":"Returns two independent analytical reports: (1) adjacent row-to-row change values for the selected field across up to 200 supplied rows, and (2) IQR-based outlier flags identifying records that fall outside the interquartile range, all computed locally with no external lookups.","failureModes":["Missing required fields (row_id, date, dataset_group) cause validation errors","Exceeding 200 rows returns an error or truncation","Invalid date format (not YYYY-MM-DD) fails schema validation","Null values in run_seconds or processed_rows may affect outlier calculations","Selecting a field enum value not in ['run_seconds','processed_rows'] returns an error","Fewer than 4 rows may produce unreliable IQR statistics"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of pipeline duration data and need both sequential delta analysis and IQR-based statistical outlier detection in a single $0.01 call, without requiring any external data sources or live lookups. Ideal for data engineering teams doing post-run pipeline health checks or regression analysis on batch job durations.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T07:24:01.808Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-adjacent-row-changes-iqr-outlier-flags-90e6d091"}