{"uid":"cap_h9wsdrjqs8FvvAB9dlXVU","slug":"pipeline-durations-analytics-secondary-measure-weighted-mean-explicit-b25fe684","name":"Pipeline Durations Analytics — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill","description":"Pipeline durations — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + Explicit missing-value fill 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-05-187?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_FbxvOi284bCWIF0376bVd","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 a secondary-measure weighted mean and fills missing values for pipeline duration records (run_seconds, processed_rows) across up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"I have 50 pipeline run records with run_seconds and processed_rows — some run_seconds values are missing. Can you compute the secondary-measure weighted mean of run_seconds weighted by processed_rows, filling missing values using median, across all my dataset groups?","exampleUseCases":[{"title":"ETL pipeline performance benchmarking","prompt":"I've got 120 pipeline run records with run_seconds and processed_rows across two dataset groups, but about 15 run_seconds values are null. Can you compute the weighted mean of run_seconds (weighted by processed_rows) and fill those missing values using the mean fill method?"},{"title":"Data volume-weighted duration analysis","prompt":"For my 80 pipeline runs tagged by date and dataset_group, can you calculate the weighted average of processed_rows using run_seconds as the secondary weight, filling any nulls with zero, so I can see which groups process the most data per second?"},{"title":"Missing-value imputation before reporting","prompt":"I have a CSV of pipeline runs — row_id, date, dataset_group, run_seconds, processed_rows — with some null processed_rows entries. Can you fill those nulls using median imputation and then give me the weighted mean report for processed_rows weighted by run_seconds?"}],"resultDescription":"Returns 2 analytical reports: (1) a secondary-measure weighted mean of the specified field (run_seconds or processed_rows), and (2) an explicit missing-value fill report using the chosen strategy (zero, mean, or median). Results are computed locally from the supplied data rows without external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, date, dataset_group) cause validation errors","Numeric values outside ±1,000,000,000,000 range rejected","Date strings not matching YYYY-MM-DD pattern fail schema validation","All values null for a field may produce degenerate weighted mean","Payment of 0.01 USDC not received — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have pipeline duration data (run_seconds, processed_rows) with potential missing values and need both a secondary-measure weighted mean and explicit null imputation in a single call. It is ideal for small-to-medium datasets (≤200 rows) where you want offline, deterministic analytics without external data lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:10.893Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-analytics-secondary-measure-weighted-mean-explicit-b25fe684"}