{"uid":"cap_vo5n0pN-M7frz7SN5pQTd","slug":"duplicate-observations-string-normalisation-numeric-interval-filter-08d09db8","name":"Duplicate Observations — String Normalisation + Numeric Interval Filter","description":"Duplicate observations — String normalisation + Numeric interval filter: String normalisation + Numeric interval filter 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-04-026?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_VANj9nkP9ZQvvXFc4tQBm","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":"Normalises dataset_group strings and filters duplicate_rows/total_rows values by a numeric interval for up to 200 duplicate-observation records, returning two analytical reports.","exampleAgentPrompt":"I have a batch of duplicate observation records across two dataset groups — 'Group A' and 'group-a' style variations — with duplicate_rows and total_rows counts. Can you normalise the group name strings and filter the duplicate_rows field to only show records between 5 and 50, across these 4 rows covering 2026-10-01 to 2026-10-04?","exampleUseCases":[{"title":"Data quality audit for ETL pipeline","prompt":"I'm auditing our ETL pipeline and have 120 rows of duplicate observation data with messy dataset_group labels like 'Sales', 'sales ', and 'SALES'. Can you normalise those strings and filter the duplicate_rows field to only show records between 0 and 100?"},{"title":"Filtering out-of-range deduplication metrics","prompt":"We track duplicate_rows and total_rows for 80 datasets every day. Can you run the numeric interval filter on total_rows, keeping only records where total_rows falls between 500 and 10000, and also normalise the dataset_group names across all rows?"},{"title":"Monthly duplicate trend review by group","prompt":"I have 150 duplicate observation records from October grouped by department names that have inconsistent capitalisation. Please normalise the dataset_group strings and filter duplicate_rows between 10 and 75 so I can see which groups are in a healthy range."}],"resultDescription":"Returns two analytical reports for the supplied duplicate-observation rows: (1) a string-normalisation report standardising dataset_group values, and (2) a numeric interval filter report identifying which rows have the specified field (duplicate_rows or total_rows) falling within the given minimum–maximum range. No external data is fetched; all analysis is performed on the supplied records.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds maximum batch size","Invalid date format (not YYYY-MM-DD) — schema validation error","minimum greater than maximum — may yield empty interval filter results","null values in duplicate_rows or total_rows — rows may be excluded from numeric filter","row_id missing or empty — required field validation failure","field enum value not 'duplicate_rows' or 'total_rows' — rejected by schema"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously normalise inconsistent string labels in a dataset_group column AND filter numeric duplicate-observation metrics (duplicate_rows or total_rows) by a numeric interval, all on supplied data without any external lookups. It is purpose-built for data quality workflows involving duplicate row auditing across named dataset groups, and delivers two complementary reports for a single low-cost call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:45:36.468Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-string-normalisation-numeric-interval-filter-08d09db8"}