{"uid":"cap_OC3djI8I3s4BbMPm1wsj5","slug":"pipeline-durations-numeric-interval-filter-explicit-missing-value-fill-32ed8e3e","name":"Pipeline Durations — Numeric Interval Filter + Explicit Missing-Value Fill","description":"Pipeline durations — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + 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-05-135?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"},"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_J6tskIyseXA_G7kmxdLdR","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":"Filters pipeline duration records to a numeric interval and fills missing values using zero, mean, or median imputation, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"Take my pipeline duration records and filter run_seconds to between 0 and 100, then fill any missing run_seconds values using median imputation — I have about 50 rows across two dataset groups.","exampleUseCases":[{"title":"Cleaning ETL run time outliers","prompt":"I have 120 pipeline run records with run_seconds values and some nulls — can you filter them to only rows where run_seconds is between 5 and 3600, and fill the missing run_seconds values using mean imputation?"},{"title":"Processed rows anomaly removal","prompt":"Filter my pipeline batch data so processed_rows stays between 1000 and 500000, and fill any null processed_rows entries with zero — I'll send you 80 rows with row IDs and dates."},{"title":"Monthly pipeline duration QA report","prompt":"Here are 150 pipeline duration records from October 2026. Please filter run_seconds to the range 0–300 and replace any missing run_seconds with the median, then give me both reports."}],"resultDescription":"Two analytical reports covering the supplied pipeline duration records after applying the specified numeric interval filter (min/max bounds on the chosen field) and explicit missing-value fill (zero, mean, or median) — up to 200 rows processed per call, all computed locally without external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","row_id missing or empty — validation error per row","date field does not match YYYY-MM-DD pattern — validation error","minimum > maximum bounds — logically invalid interval, may return empty filtered set","numeric values outside ±1,000,000,000,000 range — schema rejection","Payment of 0.01 USDC not completed — 402 response blocking execution"],"whenToPreferThis":"Choose this endpoint when you have pipeline duration records (run_seconds, processed_rows) that need both interval-based outlier filtering and missing-value imputation in a single call, with results returned as two independent analytical reports. It is ideal for lightweight data-cleaning tasks on batches up to 200 rows where no live data lookup, external enrichment, or prediction is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:45:58.841Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-numeric-interval-filter-explicit-missing-value-fill-32ed8e3e"}