{"uid":"cap_6xay6jDol-OSa8T8QwzdM","slug":"duplicate-observations-numeric-distribution-and-adjacent-row-changes-a28f4997","name":"Duplicate Observations Numeric Distribution and Adjacent Row Changes Analyzer","description":"Duplicate observations — Numeric distribution + Adjacent row changes: Numeric distribution + Adjacent row changes for supplied duplicate observations records (duplicate_rows, total_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-04-077?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","duplicate_rows","total_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"duplicate_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_rows":1,"dataset_group":"group-a","duplicate_rows":10},{"date":"2026-10-02","row_id":"row-2","total_rows":2,"dataset_group":"group-b","duplicate_rows":20},{"date":"2026-10-03","row_id":"row-3","total_rows":3,"dataset_group":"group-a","duplicate_rows":15},{"date":"2026-10-04","row_id":"row-4","total_rows":4,"dataset_group":"group-b","duplicate_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["duplicate_rows","total_rows"],"type":"string","default":"duplicate_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_tWl9uT4ix9FZj96nJEBv-","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 supplied duplicate-observation records, producing two independent reports: a numeric distribution analysis and an adjacent row change analysis for duplicate_rows or total_rows fields.","exampleAgentPrompt":"Can you run a numeric distribution and adjacent row changes analysis on my duplicate observation records? I have rows with dataset_group, date, duplicate_rows, and total_rows — here are up to 50 rows, and I want the analysis focused on the duplicate_rows field.","exampleUseCases":[{"title":"Data pipeline duplicate spike detection","prompt":"I have a list of daily pipeline run records with duplicate_rows and total_rows counts per dataset group. Can you analyze the numeric distribution and flag where duplicate_rows changes significantly between adjacent rows? Here are 80 records, analyze the duplicate_rows field."},{"title":"Dataset group quality audit","prompt":"I want to audit duplicate row counts across my dataset groups over the past quarter. Run a numeric distribution report and an adjacent row changes report on these 150 records, focusing on the total_rows field so I can see how dataset size shifts day to day."},{"title":"Anomaly triage for data ingestion logs","prompt":"We've been tracking duplicate_rows and total_rows in our ingestion logs and I suspect there are some anomalous jumps. Can you analyze these 60 records — numeric distribution plus adjacent row changes — for the duplicate_rows field so I can see where things went off?"}],"resultDescription":"Returns two independent analytical reports for the submitted rows: (1) a numeric distribution report showing statistical spread, min, max, mean, and distribution characteristics of the selected field (duplicate_rows or total_rows), and (2) an adjacent row changes report quantifying the delta between consecutive rows in the ordered dataset. All analysis is performed locally on the supplied data with no external lookups.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) cause validation errors","Invalid date format (not YYYY-MM-DD) causes schema rejection","Field selector not one of 'duplicate_rows' or 'total_rows' causes enum validation failure","Null values in the selected numeric field may affect distribution completeness","Values outside the min/max numeric bounds cause schema rejection"],"whenToPreferThis":"Choose this endpoint when you have pre-collected duplicate observation records and need two complementary offline analytical views — numeric distribution and adjacent row changes — in a single low-cost call. It is ideal for data quality pipelines, ingestion monitoring, and audit workflows where the data is already available locally and no live lookup or prediction is required. At $0.01 USDC for both reports on up to 200 rows, it is cost-effective for batch profiling tasks.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:34.747Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-numeric-distribution-and-adjacent-row-changes-a28f4997"}