{"uid":"cap_hy0PIEND2FY6bafi-s9lS","slug":"duplicate-observations-analytics-record-quality-audit-string-948d0ed6","name":"Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Numeric Distribution","description":"Duplicate observations — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution 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-04-193?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_UPvR13D8wYsUr1NtwLrcT","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 (record quality audit, string normalisation, numeric distribution) on up to 200 duplicate-observation records containing duplicate_rows and total_rows fields.","exampleAgentPrompt":"Run the three-report analytics bundle (record quality audit, string normalisation, and numeric distribution) on my duplicate observations data — here are 4 rows: row-1 on 2026-10-01 in group-a with 10 duplicate_rows out of 1 total, row-2 on 2026-10-02 in group-b with 20 duplicate_rows out of 2 total, row-3 on 2026-10-03 in group-a with 15 duplicate_rows out of 3 total, and row-4 on 2026-10-04 in group-b with 30 duplicate_rows out of 4 total; focus the numeric analysis on the duplicate_rows field.","exampleUseCases":[{"title":"Duplicate rate audit across dataset groups","prompt":"I have 50 rows of duplicate observation records split across two dataset groups, each with a date, duplicate_rows count, and total_rows count — can you run the record quality audit, string normalisation, and numeric distribution reports on them and focus the numeric analysis on duplicate_rows?"},{"title":"Data pipeline deduplication health check","prompt":"Our ETL pipeline produces a daily table of duplicate_rows vs total_rows per dataset group — please run all three analytics reports (quality audit, string normalisation, numeric distribution) on this week's 7 rows so I can spot any anomalies in the total_rows field."},{"title":"Pre-merge dataset quality profiling","prompt":"Before we merge two datasets I want to profile their overlap metrics — I have 120 duplicate observation records with row IDs, dates, group labels, and duplicate/total row counts. Run the quality audit, string normalisation check, and numeric distribution summary on them, analysing the duplicate_rows field."}],"resultDescription":"Three independent analytical reports delivered together: (1) a record quality audit covering completeness, consistency, and structural validity of the supplied rows; (2) a string normalisation report covering the dataset_group and other string fields; and (3) a numeric distribution report with quantiles, spread, and summary statistics for the selected numeric field (duplicate_rows or total_rows). All analysis is performed locally on the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected, maxItems limit exceeded","Missing required fields (row_id, dataset_group, date, duplicate_rows, or total_rows) — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","Empty rows array — minItems constraint violation","field selector value not in enum ['duplicate_rows','total_rows'] — invalid input error","Non-numeric values in duplicate_rows or total_rows beyond allowed null — type error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 duplicate-observation records and need three complementary data quality reports in a single low-cost call ($0.01 USDC). It is ideal for auditing deduplication pipeline outputs, profiling string consistency in group labels, and understanding the numeric distribution of duplicate counts — all without any external API calls, predictions, or live lookups. Prefer it over single-report alternatives when you need the combined quality + normalisation + distribution view on the same dataset in one shot.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:38.054Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-analytics-record-quality-audit-string-948d0ed6"}