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Aggregation Checks Analytics — Record Quality Audit + String Normalisation + Daily Sum Series
x402
Aggregation Checks Analytics — Record Quality Audit + String Normalisation + Daily Sum Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs three local analytical reports on supplied aggregation check records: a record quality audit, string normalisation, and a daily sum series — all for up to 200 rows of source_sum and aggregate_sum data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Aggregation checks — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum series for supplied aggregation checks records (source_sum, aggregate_sum). 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 returned for the supplied rows: (1) a record quality audit flagging data issues, (2) a string normalisation report standardising text fields such as dataset_group, and (3) a daily sum series summarising totals over time for the chosen field (source_sum or aggregate_sum). All processing is local; no external lookups or predictions are made.
Example use cases
Daily reconciliation audit for finance team
“I have a batch of up to 150 reconciliation rows with date, dataset_group, source_sum, and aggregate_sum fields — can you run the record quality audit and daily sum series reports on them so I can spot where my source totals diverge from the aggregated figures?”
String normalisation on dataset group labels
“Some of my dataset_group values look inconsistent across rows — can you run the string normalisation report on my aggregation check records so I can see which labels need cleaning? I'll supply the rows with their row_id, date, source_sum, and aggregate_sum.”
Time-series sum validation for operations pipeline
“I need to validate daily sum trends across two groups of pipeline data — can you analyse my aggregation check records and produce the daily sum series report for the aggregate_sum field, covering around 80 rows across several dates in 2026?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-17-198?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — quality audit, string normalisation, and daily sum series — on up to 200 rows of aggregation check data in a single low-cost call ($0.01 USDC). It is ideal for offline/local analysis of pre-supplied data where no live lookup, external enrichment, or prediction is required. Prefer alternatives if you need real-time data fetching, ML-based predictions, or more than 200 rows.
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- Missing required fields (row_id, dataset_group, date, source_sum, aggregate_sum) — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- source_sum or aggregate_sum outside ±1,000,000,000,000 range — rejected
- Invalid field enum value (must be source_sum or aggregate_sum) — error returned
- Payment of 0.01 USDC not received — request blocked
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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