{"uid":"cap_5L3C76egFkPbxhzv_x_g5","slug":"duplicate-observations-string-normalisation-and-adjacent-row-changes-15db689f","name":"Duplicate Observations String Normalisation and Adjacent Row Changes Analyser","description":"Duplicate observations — String normalisation + Adjacent row changes: String normalisation + 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-04-029?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_4Gg3MwQ92r62nCZCZyN57","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":"Analyses supplied duplicate-observation records to produce two local analytical reports: string normalisation and adjacent row changes, for up to 200 rows.","exampleAgentPrompt":"Can you run a string normalisation and adjacent row changes analysis on my duplicate observations data? Here are the rows: row-1 on 2024-01-01 in group-a with 10 duplicate_rows out of 500 total, row-2 on 2024-01-02 in group-a with 15 duplicate_rows out of 510 total, row-3 on 2024-01-03 in group-b with 5 duplicate_rows out of 300 total — analyse the duplicate_rows field.","exampleUseCases":[{"title":"Data pipeline duplicate quality check","prompt":"I have daily pipeline metrics for three dataset groups over the past week — can you run a string normalisation and adjacent row changes report on the duplicate_rows counts so I can spot any sudden spikes or label inconsistencies?"},{"title":"Comparing duplicate rates across groups","prompt":"Here's a table of duplicate_rows and total_rows readings for groups alpha, beta, and gamma across 10 dates — run both the string normalisation and adjacent row changes analyses so I can see how duplicates shift between consecutive days per group."},{"title":"Pre-migration data quality audit","prompt":"Before we migrate our database, I want to check for string inconsistencies in the dataset group names and sudden jumps in duplicate row counts — can you analyse these 50 rows using the duplicate observations reporter, focusing on the total_rows field?"}],"resultDescription":"Returns two analytical reports generated locally from the supplied data: (1) a string normalisation report identifying inconsistencies or variations in string fields across the duplicate observation records, and (2) an adjacent row changes report showing how the selected numeric field (duplicate_rows or total_rows) changes between consecutive rows. No external lookups or predictions are made.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds maximum batch limit","Invalid date format (not YYYY-MM-DD) — validation error","Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) — schema validation failure","Numeric values outside allowed range — validation error","Invalid field selector (not 'duplicate_rows' or 'total_rows') — enum validation error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline analytical reports on duplicate observation data — specifically string normalisation across group labels and adjacent row change detection for a numeric field — without requiring any external data source, live lookup, or ML prediction. Ideal for data quality checks on small-to-medium batches (up to 200 rows) at very low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T06:53:27.507Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-string-normalisation-and-adjacent-row-changes-15db689f"}