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Dataset Storage Record Quality Audit, String Normalisation & Adjacent Row Changes
x402
Dataset Storage Record Quality Audit, String Normalisation & Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs three analytical reports on up to 200 dataset storage records: a record quality audit, string normalisation, and adjacent row change detection for used_mb or row_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied dataset storage records (used_mb, row_count). 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 based solely on the supplied data: (1) a record quality audit highlighting missing, null, or out-of-range values; (2) a string normalisation report for text fields like dataset_group; and (3) an adjacent row changes report showing differences between consecutive rows for the selected numeric field (used_mb or row_count). No external data or predictions are included.
Example use cases
Storage usage anomaly detection
“I have a list of up to 50 dataset storage records with dates, group names, and used_mb values — can you run the quality audit and adjacent row change reports on them so I can spot any sudden spikes in storage usage?”
Dataset group name normalisation
“My dataset storage logs have inconsistent group name strings across about 80 rows. Can you run the string normalisation report on them so I get standardised, clean group names?”
Row count consistency audit
“I want to check the quality and row-to-row changes in row_count across 120 dataset storage records I have — can you run all three reports focusing on the row_count field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical perspectives (quality audit, string normalisation, adjacent changes) on a small-to-medium batch of dataset storage records (up to 200 rows) in a single low-cost call. It is a pure local computation with no live lookups or external side effects, making it safe and deterministic. Prefer alternatives if you need real-time storage monitoring, predictive analytics, or analysis of more than 200 rows at once.
Side effects
- More than 200 rows supplied — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- field enum value not 'used_mb' or 'row_count' causes validation failure
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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