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Data Ingestion Batches — Record Quality Audit + String Normalisation + Category Frequencies
x402
Data Ingestion Batches — Record Quality Audit + String Normalisation + Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
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.
Price / call$0.01
Success—
StatusUnknown
How this service works
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.
Output
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.
Example use cases
Pipeline acceptance rate quality review
“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?”
Dataset group category frequency summary
“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?”
String normalisation for ingestion logs
“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?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-01-195?utm_source=zero.xyzWhen to prefer
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.
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) — 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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