{"uid":"cap_WPt71BEa3vaQyTR5Kv7kS","slug":"data-ingestion-batches-numeric-row-ordering-iqr-outlier-flags-00a7b088","name":"Data Ingestion Batches — Numeric Row Ordering + IQR Outlier Flags","description":"Data ingestion batches — Numeric row ordering + IQR outlier flags: Numeric row ordering + 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-01-119?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_Voesr8Dty9UooIwkb27LR","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":"Ranks and IQR-flags received_rows or accepted_rows values across up to 200 data ingestion batch records, returning two analytical reports per call.","exampleAgentPrompt":"I have up to 200 rows of data ingestion batch records with fields like row_id, dataset_group, date, received_rows, and accepted_rows — can you rank them numerically and flag any IQR outliers in the received_rows field?","exampleUseCases":[{"title":"Pipeline data quality audit","prompt":"Here are 150 daily ingestion batch records for our ETL pipeline — each has a row_id, dataset_group, date, received_rows, and accepted_rows. Can you rank them by received_rows and flag any IQR outliers so I can spot bad ingestion days?"},{"title":"Accepted rows anomaly detection","prompt":"I've got 80 records from our data ingestion batches across two dataset groups over the past quarter. Run the IQR outlier detection on accepted_rows and give me the numeric ordering so I can see which runs were statistically abnormal."},{"title":"Multi-group ingestion comparison","prompt":"We have ingestion data from group-a and group-b covering 60 days — row_id, date, received_rows, and accepted_rows are all filled in. Flag any IQR outliers in received_rows and rank the rows so I can compare performance across both groups."}],"resultDescription":"Two analytical reports per call: (1) numeric ordering of the selected field (received_rows or accepted_rows) across all submitted rows, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range. No external data is fetched; analysis is performed entirely on the supplied input rows.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error","Date field not in YYYY-MM-DD format — pattern validation failure","Numeric values outside allowed range (±1,000,000,000,000) — schema rejection","Invalid field selector (not 'received_rows' or 'accepted_rows') — enum validation error","All numeric values null — IQR computation may be undefined or return empty flags"],"whenToPreferThis":"Choose this endpoint when you need fast, self-contained statistical analysis (numeric ordering + IQR outlier detection) on data ingestion batch records with up to 200 rows and two numeric fields (received_rows, accepted_rows). Ideal when no live external lookup, ML prediction, or professional data assessment is needed — just local analytical computation on your supplied data. At $0.01 USDC for two reports, it is cost-effective for routine pipeline auditing or batch QA workflows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T00:46:45.541Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-numeric-row-ordering-iqr-outlier-flags-00a7b088"}