{"uid":"cap_LSkyHat47YFo29WMwNN03","slug":"duplicate-observations-quality-audit-record-quality-string-7e5c66cc","name":"Duplicate Observations Quality Audit – Record Quality, String Normalisation & IQR Outlier Flags","description":"Duplicate observations — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied duplicate observations records (duplicate_rows, total_rows). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-203?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_X9TWgoaujEQn9Vhk79sH8","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":"Runs three analytical reports on supplied duplicate-observation data: record quality audit, string normalisation, and IQR-based outlier flagging for duplicate_rows and total_rows fields.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation check, and IQR outlier detection on my duplicate observation data? I have rows with fields like row_id, dataset_group (e.g. 'group-a', 'group-b'), date, duplicate_rows, and total_rows — up to 50 rows — and I want to flag outliers in the duplicate_rows field.","exampleUseCases":[{"title":"Deduplication pipeline health check","prompt":"I've got 80 rows of deduplication tracking data with dataset_group, date, duplicate_rows, and total_rows. Can you run all three quality reports — the record audit, string normalisation, and IQR outlier flags — focusing on the duplicate_rows field, so I can see which records look anomalous?"},{"title":"Data warehouse group label normalisation","prompt":"My dataset quality log has inconsistent group labels across about 120 rows. Run the string normalisation report and record quality audit on my duplicate observation records so I can clean up the dataset_group field before loading into the warehouse."},{"title":"Monthly duplicate rate outlier review","prompt":"Can you check my last month of duplicate row counts for outliers? I have 30 rows with dates in October 2026, dataset_group values, and both duplicate_rows and total_rows filled in — flag anything that looks like an IQR outlier in total_rows and give me the full quality audit too."}],"resultDescription":"Three independent analytical reports delivered for up to 200 supplied rows: (1) a record quality audit identifying missing, malformed, or inconsistent records; (2) a string normalisation report highlighting and correcting inconsistencies in string fields like dataset_group; (3) IQR-based outlier flags on the selected numeric field (duplicate_rows or total_rows), marking statistically anomalous values. All analysis is performed on the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds maximum batch size","date field does not match YYYY-MM-DD pattern — validation error","row_id missing or empty — schema validation failure","duplicate_rows or total_rows outside allowed numeric range — rejected","field enum value not one of 'duplicate_rows' or 'total_rows' — invalid input","Payment of 0.01 USDC not completed — request blocked"],"whenToPreferThis":"Choose this endpoint when you need three bundled data quality reports — record audit, string normalisation, and IQR outlier detection — on duplicate-observation datasets (up to 200 rows) in a single low-cost call. It is ideal for deduplication pipeline monitoring, dataset hygiene checks before ETL loads, or periodic data quality reviews where no external lookup or prediction is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:11:53.982Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-quality-audit-record-quality-string-7e5c66cc"}