{"uid":"cap_mQlFjOrLbsqngD-zC2hjq","slug":"data-ingestion-batches-numeric-distribution-iqr-outlier-flags-ada0ae74","name":"Data Ingestion Batches — Numeric Distribution + IQR Outlier Flags","description":"Data ingestion batches — Numeric distribution + IQR outlier flags: Numeric distribution + IQR outlier flags for supplied data ingestion batches records (received_rows, accepted_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-01-080?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","received_rows","accepted_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"accepted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"received_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","accepted_rows":1,"dataset_group":"group-a","received_rows":10},{"date":"2026-10-02","row_id":"row-2","accepted_rows":2,"dataset_group":"group-b","received_rows":20},{"date":"2026-10-03","row_id":"row-3","accepted_rows":3,"dataset_group":"group-a","received_rows":15},{"date":"2026-10-04","row_id":"row-4","accepted_rows":4,"dataset_group":"group-b","received_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["received_rows","accepted_rows"],"type":"string","default":"received_rows"}}},"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_uJ_rmEzr9K0fn6EbVOO_3","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 numeric distribution statistics and IQR-based outlier flags for received_rows and accepted_rows fields across up to 200 data ingestion batch records.","exampleAgentPrompt":"Can you analyze the numeric distribution and flag IQR outliers for the received_rows field across these 12 ingestion batch records I have — dates from 2024-01-01 to 2024-01-12, with row_ids batch-1 through batch-12, dataset_group 'prod-pipeline', received_rows ranging from 1000 to 95000, and accepted_rows matching about 90% of each?","exampleUseCases":[{"title":"ETL pipeline batch quality audit","prompt":"I have 50 daily ingestion batch records for my data warehouse pipeline with columns row_id, dataset_group, date, received_rows, and accepted_rows — can you run a numeric distribution and IQR outlier analysis on the accepted_rows field to see which batches are anomalous?"},{"title":"Data ingestion monitoring dashboard","prompt":"Flag any outlier days in my received_rows data for the last 30 ingestion batches across two dataset groups — I want to know the distribution stats and which specific row_ids fall outside the IQR bounds."},{"title":"Batch acceptance rate anomaly detection","prompt":"I want to check the numeric distribution and detect IQR outliers in the accepted_rows values from these 80 ingestion batch records — dataset_group is 'events-stream', dates are throughout Q1 2024, and I need to know which batches look statistically unusual."}],"resultDescription":"Returns two independent analytical reports (one per numeric field): descriptive distribution statistics (min, max, mean, median, quartiles, spread) and IQR-based outlier flags identifying which row_ids fall outside expected bounds, computed locally without any external lookup or prediction.","failureModes":["Fewer than 1 row submitted — request rejected","More than 200 rows submitted — exceeds batch limit","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error","Invalid date format (not YYYY-MM-DD) — schema rejection","Numeric values outside allowed range (±1,000,000,000,000) — rejected","Invalid field enum value (not 'received_rows' or 'accepted_rows') — error"],"whenToPreferThis":"Choose this endpoint when you need fast, local IQR-based outlier detection and distribution summaries specifically for data ingestion batch metrics (received_rows and accepted_rows) across up to 200 records, without needing external lookups, ML predictions, or professional assessments. Ideal for data engineers monitoring ETL pipeline health or auditing batch ingestion quality at low cost ($0.01 per call covering both field reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:26.454Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-numeric-distribution-iqr-outlier-flags-ada0ae74"}