{"uid":"cap_-lSKsg45FTcbS3UHxArZt","slug":"pipeline-durations-numeric-interval-filter-adjacent-row-changes-61ffd45b","name":"Pipeline Durations — Numeric Interval Filter + Adjacent Row Changes","description":"Pipeline durations — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes 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-05-127?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_uQ1piwiZoWFJVxXnKMLsm","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 by filtering rows within a numeric range and detecting adjacent row changes, returning 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"I have pipeline run records with run_seconds and processed_rows — can you filter rows where run_seconds is between 10 and 150, and also show me where adjacent rows changed, across these 80 records I'm sending?","exampleUseCases":[{"title":"Flag slow pipeline runs in range","prompt":"I've got 120 pipeline execution records and I want to find all the ones where run_seconds falls between 60 and 300, and also see where the run time changed between consecutive rows — can you run both reports on this data?"},{"title":"Detect throughput anomalies between rows","prompt":"Here are my ETL pipeline records for October — filter on processed_rows between 500 and 5000, and tell me where consecutive rows show big changes in processed_rows."},{"title":"QA pipeline timing data before reporting","prompt":"Before I report on these 75 pipeline runs, I need to know which ones have run_seconds between 0 and 90, and also flag anywhere the run_seconds shifted noticeably from one row to the next."}],"resultDescription":"Two analytical reports: (1) rows from the supplied pipeline duration records where the selected field (run_seconds or processed_rows) falls within the specified numeric interval [minimum, maximum], and (2) a report of adjacent row changes showing where consecutive rows differ in the selected field's value. Up to 200 input rows processed per call.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required row fields (row_id, date, run_seconds, processed_rows, dataset_group) — validation error","minimum greater than maximum — may return empty interval filter results","Date strings not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range (-1e12 to 1e12) — schema rejection"],"whenToPreferThis":"Use this endpoint when you already have pipeline duration data in hand (run_seconds, processed_rows) and need to (1) isolate records within a specific numeric range and (2) detect value changes between consecutive rows — all in a single paid call returning 2 reports. Prefer this over general-purpose analytics when your data fits the pipeline duration schema and you need both interval filtering and adjacent-change detection together for up to 200 rows at a flat rate.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:41:11.927Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-numeric-interval-filter-adjacent-row-changes-61ffd45b"}