{"uid":"cap_tIigUF6A2um1yoqc7eDJ3","slug":"pipeline-durations-analytics-base-100-series-date-by-category-pivot-dfc46881","name":"Pipeline Durations Analytics: Base-100 Series + Date-by-Category Pivot","description":"Pipeline durations — Base-100 series + Date by category pivot: Base-100 series + Date by category pivot 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-05-167?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"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_JRnfX5BeBVtusMbI7scgY","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":"Generates two analytical reports — a Base-100 indexed time series and a date-by-category pivot table — from supplied pipeline duration records (run_seconds, processed_rows).","exampleAgentPrompt":"Take my pipeline run records — rows with dates, dataset groups like 'group-a' and 'group-b', and run_seconds values — and give me both a Base-100 indexed series starting from the first row and a date-by-category pivot, using run_seconds as the metric.","exampleUseCases":[{"title":"ETL pipeline performance trend analysis","prompt":"I have up to 50 rows of daily ETL run data with columns date, dataset_group, and run_seconds. Index the run_seconds to Base-100 starting at row 0 and also give me a pivot showing run_seconds by date and group so I can spot which group is slowing down."},{"title":"Data throughput comparison across groups","prompt":"Here are my pipeline records with processed_rows, dates, and dataset groups. Can you generate a Base-100 series for processed_rows from the first record and a date-by-category pivot so I can compare throughput trends across groups?"},{"title":"Baseline deviation report for ops review","prompt":"I want to share a pipeline health report with my team. Run the Base-100 series and date-by-category pivot on these 120 pipeline records using run_seconds, with the base index set to row 5 so we normalize against a known stable run."}],"resultDescription":"Returns two independent analytical reports: (1) a Base-100 indexed time series showing relative change in the selected metric (run_seconds or processed_rows) over time, rebased to the specified index row; and (2) a date-by-category pivot table aggregating the metric across dataset_group and date dimensions. Covers up to 200 input rows.","failureModes":["Missing required fields (row_id, date, dataset_group, run_seconds, processed_rows) cause row rejection or error","More than 200 rows submitted — only up to 200 are processed","Invalid date format (not YYYY-MM-DD) may cause parsing failures","base_index out of range for the provided dataset returns an error","Null values in the selected metric field may produce gaps in the Base-100 series"],"whenToPreferThis":"Choose this endpoint when you have pipeline duration or throughput records and need both a normalized trend index and a categorical pivot in a single low-cost call. It is purpose-built for pipeline metrics (run_seconds, processed_rows) with a fixed schema, making it faster and cheaper than general-purpose analytics tools for this specific use case. Not suitable for live data lookup, predictions, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T07:24:26.029Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-analytics-base-100-series-date-by-category-pivot-dfc46881"}