{"uid":"cap_l_wGcY-h-0GGOafHdcSCH","slug":"pipeline-durations-iqr-outlier-flags-and-missing-value-fill-321a3155","name":"Pipeline Durations IQR Outlier Flags and Missing Value Fill","description":"Pipeline durations — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + 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-05-180?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_JgXzpvOlkzoR2e-NkUhOW","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 (run_seconds, processed_rows) to flag IQR-based outliers and fill missing values using a chosen imputation method, producing two analytical reports per call.","exampleAgentPrompt":"I have up to 200 rows of pipeline run records with run_seconds and processed_rows — can you flag the IQR outliers and fill any missing run_seconds values using median imputation?","exampleUseCases":[{"title":"ETL pipeline anomaly audit","prompt":"Here are 150 rows of daily ETL pipeline runs with run_seconds and processed_rows — flag which runs are IQR outliers on run_seconds and fill any missing run_seconds with the median."},{"title":"Data quality fix before reporting","prompt":"I've got pipeline duration logs for the last quarter across multiple dataset groups and some rows have null processed_rows — can you fill those nulls using mean imputation and also flag any outliers using IQR on processed_rows?"},{"title":"Batch pipeline health check","prompt":"I need to clean up 80 rows of pipeline records from group-a and group-b: flag any run_seconds that are IQR outliers and fill missing run_seconds with zero so I can report on pipeline health."}],"resultDescription":"Two analytical reports per call: (1) an IQR outlier flag report marking which rows have anomalous values on the chosen field, and (2) a missing-value fill report showing imputed values (zero, mean, or median) for null entries in the chosen field — all computed locally against the supplied rows, with no external data fetched.","failureModes":["Fewer than 1 row submitted — rejected by minItems constraint","More than 200 rows submitted — rejected by maxItems constraint","Invalid date format (not YYYY-MM-DD) — schema validation error","Unknown fill method or field enum value — schema validation error","All values null for the target field — median/mean may be undefined or fallback to zero","row_id missing or empty — required field validation error"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based outlier detection and missing-value imputation on pipeline duration data (run_seconds or processed_rows) in a single $0.01 call for up to 200 rows. It is purpose-built for data engineering / ETL quality workflows and returns two co-computed reports without any external lookups or side effects. Prefer it over general-purpose stats APIs when your data is already structured as pipeline run records with the exact fields (run_seconds, processed_rows, dataset_group, date).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:12.792Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-iqr-outlier-flags-and-missing-value-fill-321a3155"}