{"uid":"cap_O9f385BV80lPKEahKVlQf","slug":"join-coverage-analytics-record-quality-audit-string-normalisation-a2bf9e1a","name":"Join Coverage Analytics: Record Quality Audit, String Normalisation & Trailing Mean Series","description":"Join coverage — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series 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-09-199?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"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_1xJnW-XhMeH1VzmvtPEKb","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 on join coverage data (matched_rows, source_rows): a record quality audit, string normalisation, and trailing mean time series — all offline, no external lookups.","exampleAgentPrompt":"Can you audit the quality of my join coverage records, normalise the dataset_group strings, and compute a trailing mean (window=3) for matched_rows? Here are my rows: row-1, 2026-10-01, group-a, matched=10, source=100; row-2, 2026-10-02, group-b, matched=20, source=200; row-3, 2026-10-03, group-a, matched=15, source=150.","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"I've got a batch of join coverage records from my ETL pipeline — can you run a record quality audit on them and flag any issues with matched_rows vs source_rows? Here are up to 50 rows with row_id, date, dataset_group, matched_rows, and source_rows."},{"title":"Rolling average trend for coverage monitoring","prompt":"Can you compute a trailing mean series with a window of 5 for the source_rows field across my join coverage dataset? I have 30 daily records with row_id, date, dataset_group, matched_rows, and source_rows that I'd like to pass in."},{"title":"Dataset group label normalisation","prompt":"My join coverage data has inconsistent dataset_group strings — things like 'Group-A', 'group_a', 'GROUP A'. Can you run the string normalisation report on my 40 rows and tell me what the cleaned labels look like?"}],"resultDescription":"Three independent analytical reports for the supplied join coverage rows: (1) a record quality audit flagging nulls, out-of-range values, and anomalies in matched_rows/source_rows; (2) a string normalisation report on dataset_group and row_id fields; (3) a trailing mean time series for the chosen field (matched_rows or source_rows) computed with the specified window size. All analysis is performed locally with no external calls.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","date fields not matching YYYY-MM-DD pattern — validation error","matched_rows or source_rows outside allowed numeric bounds — schema rejection","window value outside 2–200 range — parameter error","field enum value not 'matched_rows' or 'source_rows' — invalid input","Missing required fields (row_id, dataset_group, date) in any row — schema error"],"whenToPreferThis":"Choose this endpoint when you need to batch-audit the quality of join coverage records (matched vs source rows), normalise string fields, and compute a rolling mean — all in one offline call for up to 200 rows at a fixed cost of $0.01 USDC. Prefer it over general-purpose analytics when you specifically need these three combined reports on join coverage metrics without any external data lookups or live predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:39.283Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-record-quality-audit-string-normalisation-a2bf9e1a"}