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Data Ingestion Batches — Record Quality Audit + String Normalisation + Numeric Interval Filter
x402
Data Ingestion Batches — Record Quality Audit + String Normalisation + Numeric Interval Filter is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-17).
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.
Price / call$0.01
Success—
StatusUnknown
How this service works
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.
Output
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.
Example use cases
Daily ETL pipeline data quality check
“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.”
Anomaly detection on received row counts
“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.”
Pre-load validation for data warehouse ingestion
“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.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-01-197?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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