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Data Ingestion Batches — Record Quality Audit, String Normalisation & Date-by-Category Pivot
x402
Data Ingestion Batches — Record Quality Audit, String Normalisation & Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs three analytical reports on up to 200 data ingestion batch rows: a record quality audit, string normalisation, and a date-by-category pivot for received_rows or accepted_rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data ingestion batches — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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: (1) a record quality audit flagging completeness and consistency issues across the supplied rows, (2) a string normalisation report standardising dataset_group and other string fields, and (3) a date-by-category pivot table summarising either received_rows or accepted_rows grouped by date and dataset_group. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
ETL pipeline quality check
“I've got 120 rows from last week's data ingestion batches — each with a row_id, dataset_group, date, received_rows, and accepted_rows. Can you audit the record quality, normalise the dataset_group strings, and pivot accepted_rows by date and category so I can spot which groups had the most rejections?”
Daily batch acceptance rate review
“Run a quality audit plus a date-by-category pivot on these 80 ingestion batch records using the received_rows field — I need to see how many rows were received per group per day and whether any records look malformed or incomplete.”
Data onboarding group comparison
“We just onboarded three new data partners and I have 45 rows of ingestion data with received_rows and accepted_rows for each. Can you do a quality audit, clean up the dataset_group names with string normalisation, and give me a date pivot so I can compare acceptance rates across groups over the past two weeks?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-01-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 data ingestion batch records and need three simultaneous analyses — quality audit, string normalisation, and date pivot — in a single cheap call ($0.01 USDC). It is ideal for ETL pipeline monitoring, batch acceptance rate review, and data onboarding quality checks where no live external lookup or ML prediction is needed and all computation is local to the supplied data.
Side effects
- More than 200 rows submitted — request rejected, trim to 200 max
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error
- Date field not in YYYY-MM-DD format — pattern validation failure
- Numeric fields outside allowed range (±1,000,000,000,000) — schema rejection
- Empty rows array — minItems:1 constraint failure
- Invalid field enum value (not 'received_rows' or 'accepted_rows') — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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