{"uid":"cap_m2sY-Dtxt7FRm396VwiLV","slug":"pipeline-durations-analytics-numeric-row-ordering-adjacent-row-changes-ccee1cef","name":"Pipeline Durations Analytics — Numeric Row Ordering + Adjacent Row Changes","description":"Pipeline durations — Numeric row ordering + Adjacent row changes: Numeric row ordering + 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-05-116?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_rdoNuzuH-gqtboq00n2av","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 to produce numeric row ordering and adjacent row change reports for run_seconds or processed_rows fields.","exampleAgentPrompt":"Analyze my pipeline run data and give me two reports — one that ranks each run numerically by run_seconds, and one showing the change in run_seconds between adjacent rows. Here are my records: row-1 on 2026-10-01 for group-a with 120 seconds and 5000 rows, row-2 on 2026-10-02 for group-b with 95 seconds and 4800 rows, row-3 on 2026-10-03 for group-a with 200 seconds and 6200 rows.","exampleUseCases":[{"title":"Spike detection in nightly ETL runs","prompt":"I have 50 nightly pipeline runs logged with their run_seconds and processed_rows. Can you rank them by run_seconds and also show me the change in run_seconds between each consecutive run so I can spot where things suddenly slowed down?"},{"title":"Comparing batch sizes across pipeline groups","prompt":"Here are 80 pipeline execution records across two dataset groups. Analyze the processed_rows field — give me a numeric ranking of all rows and show me how processed_rows changed from one run to the next."},{"title":"Auditing sequential duration drift in pipelines","prompt":"I want to understand whether my pipeline durations are gradually creeping up over time. I have 150 records with run_seconds — can you rank them numerically and calculate the adjacent row differences so I can see the drift?"}],"resultDescription":"Returns two independent analytical reports: (1) a numeric row ordering report ranking each pipeline record by the selected field (run_seconds or processed_rows), and (2) an adjacent row changes report showing the delta between consecutive rows for the selected field. Both reports are computed locally against the supplied data with no external lookups.","failureModes":["Missing required fields (row_id, date, run_seconds, processed_rows) causes validation failure","More than 200 rows submitted exceeds the limit and will be rejected","Invalid date format (not YYYY-MM-DD) causes schema validation error","Null values in the selected field may produce null deltas in the adjacent row changes report","Invalid field enum value (anything other than run_seconds or processed_rows) causes rejection"],"whenToPreferThis":"Choose this endpoint when you need both numeric ranking and sequential delta analysis on pipeline duration data (run_seconds or processed_rows) in a single low-cost call. It is ideal for ETL monitoring, performance auditing, and detecting anomalies in batch pipeline runs without needing a database or external analytics platform. Best when you have up to 200 records ready to submit and want two complementary views — ordered ranking plus row-over-row changes — delivered together.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:41:01.889Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-analytics-numeric-row-ordering-adjacent-row-changes-ccee1cef"}