{"uid":"cap_Om245z2UfKj8ADhuhOrbO","slug":"transformation-throughput-exact-row-deduplication-iqr-outlier-flags-a1c21c76","name":"Transformation Throughput — Exact Row Deduplication + IQR Outlier Flags","description":"Transformation throughput — Exact row deduplication + IQR outlier flags: Exact row deduplication + IQR outlier flags for supplied transformation throughput records (processed_rows, duration_seconds). 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-13-049?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","processed_rows","duration_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"duration_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","processed_rows":10,"duration_seconds":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","processed_rows":20,"duration_seconds":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","processed_rows":15,"duration_seconds":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","processed_rows":30,"duration_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["processed_rows","duration_seconds"],"type":"string","default":"processed_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_9hrsuiw9CUw4lHC92JFLQ","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":"Deduplicates transformation throughput records and flags statistical outliers using IQR analysis for up to 200 rows of processed_rows or duration_seconds data.","exampleAgentPrompt":"Can you deduplicate my transformation throughput records and flag IQR outliers on the processed_rows field? Here are up to 200 rows with row_id, dataset_group, date, processed_rows, and duration_seconds.","exampleUseCases":[{"title":"ETL pipeline deduplication audit","prompt":"I have 150 rows of ETL run records with row_id, dataset_group, date, processed_rows, and duration_seconds — can you deduplicate them and flag any IQR outliers on the processed_rows field so I know which pipeline runs are anomalous?"},{"title":"Duration outlier detection across groups","prompt":"Here are my batch transformation logs for last month, up to 200 rows with row_id, dataset_group, date, processed_rows, and duration_seconds. Deduplicate the records and flag any IQR outliers on duration_seconds so I can identify unusually slow or fast runs."},{"title":"Data quality check before reporting","prompt":"Before I aggregate my throughput numbers, can you run a dedup and IQR outlier check on my 80-row transformation dataset? I want to analyze the processed_rows field and get two analytical reports out of it."}],"resultDescription":"Two analytical reports covering: (1) exact row deduplication results identifying and removing duplicate records, and (2) IQR-based outlier flags on the selected numeric field (processed_rows or duration_seconds), all computed locally without external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, date, processed_rows, duration_seconds) — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure","Numeric values outside allowed range — rejected","Insufficient data variance for IQR computation — may produce degenerate outlier flags","field enum value not one of processed_rows or duration_seconds — schema error"],"whenToPreferThis":"Choose this endpoint when you need a combined deduplication and IQR outlier detection pass on transformation throughput records (processed_rows or duration_seconds) in a single low-cost call. Ideal for data engineers auditing ETL pipeline logs before aggregation or reporting, when data is already collected and no live lookup or external enrichment is needed. Best for datasets up to 200 rows where two analytical reports are required at minimal cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:00.550Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-exact-row-deduplication-iqr-outlier-flags-a1c21c76"}