{"uid":"cap_7QaNwI2mj2I0fCtwnJoob","slug":"data-ingestion-batches-record-quality-audit-string-normalisation-b5ebd9f5","name":"Data Ingestion Batches — Record Quality Audit + String Normalisation + Numeric Interval Filter","description":"Data ingestion batches — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter 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-01-197?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},"field":{"enum":["received_rows","accepted_rows"],"type":"string","default":"received_rows"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_NZkDKGl5psEazZHThi8EN","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 on data ingestion batch records: a record quality audit, string normalisation check, and numeric interval filter for received_rows or accepted_rows values, for up to 200 rows per call.","exampleAgentPrompt":"Can you audit the quality of my ingestion batch records, normalise the dataset_group strings, and filter out any rows where received_rows falls outside 0 to 5000? Here are my rows: row-1 group-a 2025-01-10 received=4500 accepted=4200, row-2 group-b 2025-01-11 received=6000 accepted=5800, row-3 group-a 2025-01-12 received=300 accepted=290.","exampleUseCases":[{"title":"Daily ETL pipeline data quality check","prompt":"I have today's ingestion batch log with 50 rows — can you run a quality audit, normalise the dataset_group strings, and flag any rows where accepted_rows is outside the range 0 to 100000? Here's the data: row-1, sales-APAC, 2025-06-01, received=95000, accepted=92000; row-2, sales apac, 2025-06-01, received=500, accepted=480."},{"title":"Anomaly detection on received row counts","prompt":"Check my ingestion records and filter out any rows where received_rows falls between 1000 and 50000 — I want to see which entries are outside that range. Also run the quality audit and string normalisation on all 30 rows. Dataset includes groups like 'CRM_v2', 'crm v2', 'CRM-V2' across dates in May 2025."},{"title":"Pre-load validation for data warehouse ingestion","prompt":"Before I load these 120 ingestion batch records into the warehouse, can you audit their quality, normalise the dataset group labels, and filter accepted_rows to only show rows between 0 and 999999? Rows span groups like 'finance', 'Finance ', 'FINANCE' from January 2025."}],"resultDescription":"Returns three independent analytical reports for the submitted rows: (1) a record quality audit identifying missing, malformed, or suspect values; (2) a string normalisation report on dataset_group and date string fields; and (3) a numeric interval filter report showing which rows have the selected field (received_rows or accepted_rows) within the specified minimum–maximum range. All analysis is performed locally with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) on any row — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside ±1,000,000,000,000 bounds — rejected by schema","Invalid field enum value (not 'received_rows' or 'accepted_rows') — error response","Empty rows array (minItems:1 violated) — request rejected"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, and numeric interval filter) on data ingestion batch records in a single cheap call ($0.01 USDC for up to 200 rows). Ideal for ETL pipeline validation, pre-load data quality checks, and batch anomaly detection on received_rows/accepted_rows metrics without requiring any external data sources or live lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T00:52:36.410Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-record-quality-audit-string-normalisation-b5ebd9f5"}