{"uid":"cap_HvZq2iyGBUYYA1h_zBDqv","slug":"data-ingestion-batches-record-quality-audit-string-normalisation-41beb333","name":"Data Ingestion Batches — Record Quality Audit + String Normalisation + Category Frequencies","description":"Data ingestion batches — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied data ingestion batches records (received_rows, accepted_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-01-195?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","received_rows","accepted_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"accepted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"received_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","accepted_rows":1,"dataset_group":"group-a","received_rows":10},{"date":"2026-10-02","row_id":"row-2","accepted_rows":2,"dataset_group":"group-b","received_rows":20},{"date":"2026-10-03","row_id":"row-3","accepted_rows":3,"dataset_group":"group-a","received_rows":15},{"date":"2026-10-04","row_id":"row-4","accepted_rows":4,"dataset_group":"group-b","received_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_uFVyX88OjMazkOfHFYoyG","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, and category frequencies) on up to 200 data ingestion batch rows containing received_rows and accepted_rows fields.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and category frequency breakdown on my ingestion batch data? Here are up to 200 rows with row_id, dataset_group, date, received_rows, and accepted_rows — I want all three reports in one shot.","exampleUseCases":[{"title":"Pipeline acceptance rate quality review","prompt":"I have a batch of ingestion records with row IDs, dataset groups, dates, and received vs accepted row counts — can you run a quality audit on them to flag any data issues and tell me what fraction of rows are being accepted?"},{"title":"Dataset group category frequency summary","prompt":"Can you analyse these ingestion batch records and give me a category frequency breakdown by dataset_group so I can see which groups are most and least active across my pipeline runs?"},{"title":"String normalisation for ingestion logs","prompt":"My ingestion log has inconsistent casing and formatting in the dataset_group and row_id fields — can you run a string normalisation report on these batch rows so I can clean them up before loading into the warehouse?"}],"resultDescription":"Three independent analytical reports generated from the supplied rows: (1) a record quality audit flagging issues such as nulls, out-of-range values, and row completeness; (2) a string normalisation report identifying and correcting inconsistencies in string fields like dataset_group and row_id; (3) a category frequency report showing the distribution of values across categorical fields such as dataset_group. All reports are computed locally with no external lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","received_rows or accepted_rows outside allowed numeric range — validation error","Empty rows array (minItems: 1 not met) — request rejected"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, category frequencies) on data ingestion batch records in a single low-cost call. It is ideal for ETL pipeline monitoring, data quality checks before warehouse loading, and understanding dataset group distributions — all without requiring external lookups or live data connections. Prefer this over general-purpose analytics tools when working specifically with ingestion batch data shaped around received_rows and accepted_rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T18:48:36.437Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-record-quality-audit-string-normalisation-41beb333"}