{"uid":"cap_EvxwWS_jX_hsur7Awb8iQ","slug":"transformation-throughput-analytics-adjacent-row-changes-iqr-outlier-404bc826","name":"Transformation Throughput Analytics — Adjacent Row Changes & IQR Outlier Flags","description":"Transformation throughput — Adjacent row changes + IQR outlier flags: Adjacent row changes + 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-13-157?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_aNWzCd8eHa6Z6ARrjssN7","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 transformation throughput records by computing adjacent row changes and IQR-based outlier flags for processed_rows or duration_seconds fields across up to 200 rows.","exampleAgentPrompt":"Can you analyze my transformation throughput records for adjacent row changes and IQR outlier flags? I have 5 rows with date, row_id, dataset_group, processed_rows, and duration_seconds — please flag outliers on the processed_rows field.","exampleUseCases":[{"title":"ETL pipeline anomaly detection","prompt":"I have daily ETL job stats across two dataset groups for the past month — can you compute adjacent row changes and flag IQR outliers on processed_rows so I can see which days had abnormal throughput?"},{"title":"Pipeline duration spike investigation","prompt":"Here are 50 transformation job records with their duration_seconds. Can you run the adjacent row change and IQR outlier analysis on duration_seconds to find which runs took suspiciously long or short?"},{"title":"Data quality audit across groups","prompt":"I want to audit my transformation throughput logs — I have about 150 rows tagged with dataset_group and processed_rows. Can you flag outlier rows using IQR and show me where big changes happened between consecutive entries?"}],"resultDescription":"Returns two analytical reports: (1) adjacent row change values showing the difference between consecutive records for the selected field, and (2) IQR-based outlier flags marking records that fall outside the interquartile range bounds — both computed locally over the supplied dataset of up to 200 rows.","failureModes":["Missing required fields (row_id, date, processed_rows, duration_seconds) cause validation errors","More than 200 rows submitted exceeds the maxItems limit","Invalid date format (not YYYY-MM-DD) fails pattern validation","Null values in the selected metric field may affect IQR computation","Unsupported field enum value returns an error","Payment of 0.01 USDC not received causes request rejection"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical analysis of transformation throughput time-series data — specifically adjacent-row delta computation and IQR outlier flagging — without requiring external data lookups, ML predictions, or professional assessments. Best for ETL/pipeline monitoring use cases with up to 200 rows at low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:18.593Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-analytics-adjacent-row-changes-iqr-outlier-404bc826"}