{"uid":"cap_1wWYT3pBQHw0_676hShBE","slug":"data-ingestion-batches-category-frequencies-iqr-outlier-flags-f41e414d","name":"Data Ingestion Batches — Category Frequencies + IQR Outlier Flags","description":"Data ingestion batches — Category frequencies + IQR outlier flags: Category frequencies + 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-01-107?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_dV73LENHrFekOeFwWmd0P","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 up to 200 data ingestion batch records to produce category frequency distributions and IQR-based outlier flags for received_rows or accepted_rows metrics.","exampleAgentPrompt":"Analyze my ingestion batch records — I have rows with dataset_group, date, received_rows, and accepted_rows — and give me category frequencies per dataset group plus IQR outlier flags for the received_rows field. Here are up to 200 rows of batch data.","exampleUseCases":[{"title":"Pipeline ingestion quality audit","prompt":"I have 150 rows of daily ingestion batch logs with dataset_group, date, received_rows, and accepted_rows. Can you compute how frequently each dataset group appears and flag any received_rows values that are IQR outliers?"},{"title":"Accepted rows anomaly detection","prompt":"Here are 80 ingestion batch records across several dataset groups. Run the IQR outlier detection on the accepted_rows field and also show me the category frequency breakdown by dataset group."},{"title":"Daily ETL monitoring report","prompt":"I'm monitoring our ETL pipeline and want to know which dataset groups dominate our ingestion batches this month and whether any batches have suspiciously low or high received_rows — flag the outliers using IQR and give me the group frequency counts."}],"resultDescription":"Returns two independent analytical reports for the supplied batch records: (1) category frequency counts showing how often each dataset_group value appears, and (2) IQR-based outlier flags for the selected numeric field (received_rows or accepted_rows), marking rows whose values fall outside the interquartile range bounds. All analysis is performed locally with no external lookups.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — schema validation failure","Invalid date format (not YYYY-MM-DD) — pattern validation error","Numeric values out of allowed range (±1,000,000,000,000) — validation error","All values identical — IQR is zero, no outliers detectable"],"whenToPreferThis":"Choose this endpoint when you need a quick, offline statistical profile of data ingestion batch records specifically — combining group-level frequency analysis with IQR outlier detection in a single call for up to 200 rows. Prefer it over generic analytics tools when your data conforms to the ingestion batch schema (received_rows, accepted_rows, dataset_group, date) and you want two bundled reports at low cost with no external dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:24.804Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-category-frequencies-iqr-outlier-flags-f41e414d"}