{"uid":"cap_9_ckqyRokoVDY1WkwCpuy","slug":"duplicate-observations-data-quality-analyzer-82f8fd7a","name":"Duplicate Observations Data Quality Analyzer","description":"Duplicate observations — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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-04-191?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}}},"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_qe9rzLRfp7fxAFx4jqLtP","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 record quality audit, string normalization, and exact row deduplication analysis on supplied duplicate-observation records, returning three independent analytical reports.","exampleAgentPrompt":"Can you run a full data quality audit on my duplicate observations dataset — it has 4 rows with dataset groups 'alpha' and 'beta', dates from 2026-10-01 to 2026-10-04, and duplicate_rows counts of 10, 20, 15, and 30 against total_rows of 1, 2, 3, and 4? I want the record quality audit, string normalization, and exact deduplication reports.","exampleUseCases":[{"title":"ETL pipeline data quality gate","prompt":"Before loading my pipeline output, can you audit these 50 records for data quality issues, normalize any inconsistent strings, and flag exact duplicate rows? Each record has a row_id, a dataset_group like 'transactions' or 'events', a date in YYYY-MM-DD format, and duplicate_rows and total_rows counts."},{"title":"Monthly duplicate rate review","prompt":"I have a monthly summary of duplicate observations across 12 dataset groups — each row has a date, group name, duplicate_rows, and total_rows. Can you run the record quality audit, string normalization check, and exact deduplication analysis so I can see where my data has the worst duplication problems?"},{"title":"Data warehouse cleaning report","prompt":"We're cleaning up our data warehouse and I need to audit 150 observation records for duplicates and string inconsistencies. Each record has a row_id, a dataset_group, a date, and counts for duplicate_rows and total_rows. Can you give me all three reports — quality audit, string normalization, and exact row deduplication?"}],"resultDescription":"Three independent analytical reports covering: (1) a record quality audit highlighting data issues in the supplied rows, (2) a string normalization report identifying and correcting inconsistent string fields such as dataset_group, and (3) an exact row deduplication report identifying identical rows among the supplied records — all computed locally without external lookups.","failureModes":["More than 200 rows submitted — exceeds the maxItems:200 limit, request will be rejected","Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) cause validation errors","Date field not matching YYYY-MM-DD pattern causes schema validation failure","Numeric fields outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) cause validation errors","Empty rows array (minItems:1 not satisfied) returns an error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 structured duplicate-observation records with row_id, dataset_group, date, duplicate_rows, and total_rows fields and need all three of: record quality auditing, string normalization, and exact deduplication in a single low-cost call ($0.01 USDC). Prefer this over general-purpose data tools when you specifically need a combined deduplication + normalization + quality audit report computed locally without any external lookups or live data connections.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:42.208Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-data-quality-analyzer-82f8fd7a"}