{"uid":"cap_NyLCxgZF7ErKqIsKPR-X5","slug":"join-coverage-analytics-record-quality-audit-string-normalisation-dbdb6736","name":"Join Coverage Analytics: Record Quality Audit, String Normalisation & Adjacent Row Changes","description":"Join coverage — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied join coverage records (matched_rows, source_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-09-200?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","matched_rows","source_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"matched_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_rows":1,"matched_rows":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_rows":2,"matched_rows":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_rows":3,"matched_rows":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_rows":4,"matched_rows":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["matched_rows","source_rows"],"type":"string","default":"matched_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_h2n2JieovgLg4xFg10w9H","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 local analytical reports (record quality audit, string normalisation, and adjacent row changes) on supplied join coverage data (matched_rows, source_rows) for up to 200 rows.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation check, and adjacent row changes report on my join coverage data — I have rows with matched_rows and source_rows values across groups like 'group-a' and 'group-b' from 2026-10-01 to 2026-10-04, and I want to analyse the matched_rows field?","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"I've got about 50 rows of join coverage data with matched_rows and source_rows across two dataset groups from last month — can you run a full record quality audit, string normalisation check, and adjacent row change detection on them, focusing on the matched_rows field?"},{"title":"Detect anomalous row-to-row changes","prompt":"Can you check my join coverage records for unexpected adjacent row changes in the source_rows field? I have 120 rows across dataset groups 'sales' and 'returns' dated from 2025-01-01 to 2025-04-30."},{"title":"String normalisation for dataset group labels","prompt":"My join coverage table has inconsistent dataset_group strings across about 80 rows — can you run string normalisation plus a record quality audit on the matched_rows field to flag any issues?"}],"resultDescription":"Returns three independent analytical reports: a record quality audit (flagging nulls, outliers, and invalid values), a string normalisation report (identifying inconsistencies in text fields like dataset_group), and an adjacent row changes report (detecting row-to-row deltas in the selected numeric field). All analysis is performed locally on the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Invalid date format (not YYYY-MM-DD) causes validation failure","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes schema rejection","Non-numeric values in matched_rows or source_rows fields fail type validation","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical passes — quality audit, string normalisation, and adjacent row change detection — on join coverage data (matched_rows/source_rows) in a single $0.01 call for up to 200 rows, without needing live data lookups or external enrichment. Ideal for lightweight, self-contained data quality checks in ETL or data pipeline workflows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:37.865Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-record-quality-audit-string-normalisation-dbdb6736"}