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Data Ingestion Batches — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Data Ingestion Batches — Record Quality Audit + String Normalisation + Trailing Mean Series 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 on data ingestion batch records: a record quality audit, string normalisation, and trailing mean series computation for received_rows and accepted_rows fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data ingestion batches — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series 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 supplied batch rows: (1) a record quality audit flagging issues in received_rows/accepted_rows values, (2) a string normalisation report on fields like dataset_group, and (3) a trailing mean series for the selected numeric field (received_rows or accepted_rows) over the specified window size. All computed locally with no external lookups.
Example use cases
Pipeline ingestion quality health check
“I have a week's worth of ingestion batch records — can you audit them for quality issues, normalise the dataset_group strings, and give me a trailing mean series with window=5 on received_rows? I'll paste the rows: row-1/group-A/2026-11-01/received=500/accepted=480, row-2/Group-a/2026-11-02/received=520/accepted=500, row-3/group_a/2026-11-03/received=490/accepted=470.”
Accepted rows trend smoothing for reporting
“Can you compute a trailing mean series with a window of 4 on the accepted_rows field from my ingestion data, and also run a quality audit and string normalisation on the same batch? Rows: row-1/dataset-X/2026-10-01/received=1000/accepted=950, row-2/Dataset-X/2026-10-02/received=1100/accepted=1040, row-3/dataset_x/2026-10-03/received=980/accepted=920, row-4/DATASET-X/2026-10-04/received=1050/accepted=1000.”
Multi-group ingestion anomaly detection
“I'm seeing inconsistent dataset_group labels and some suspiciously low accepted_rows counts — can you run a record quality audit, normalise the strings, and give a trailing mean (window=2) on received_rows across these 5 batch records? row-1/group-Alpha/2026-09-01/received=300/accepted=10, row-2/Group_Alpha/2026-09-02/received=310/accepted=305, row-3/group-beta/2026-09-03/received=0/accepted=0, row-4/GROUP-BETA/2026-09-04/received=280/accepted=270, row-5/group-alpha/2026-09-05/received=290/accepted=285.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-01-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three analytical views on data ingestion batch records in a single low-cost call: quality auditing, string normalisation, and rolling mean computation. It is ideal for ETL monitoring, pipeline health checks, and data quality reviews where received_rows and accepted_rows are the core metrics, and where no live external data, predictions, or professional assessments are needed.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) on any row — validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- Window size less than 2 or greater than 200 — parameter error
- Numeric values outside allowed range (±1,000,000,000,000) — schema rejection
- Additional properties on row objects not permitted — additionalProperties error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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