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Dataset Storage Analytics — Record Quality Audit, String Normalisation & Date-by-Category Pivot
x402
Dataset Storage Analytics — 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 2026-10-15).
Runs three analytical reports (record quality audit, string normalisation, and date-by-category pivot) on up to 200 rows of dataset storage records containing used_mb and row_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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
Three independent analytical reports returned together: (1) a record quality audit highlighting missing, null, or anomalous values in the supplied rows; (2) a string normalisation report on text fields such as dataset_group and row_id; and (3) a date-by-category pivot summarising the selected numeric field (used_mb or row_count) broken down by date and dataset_group. All reports are computed locally with no external data lookups.
Example use cases
Storage usage data quality check
“I have 50 rows of dataset storage records with row_id, dataset_group, date, used_mb, and row_count columns — can you run a record quality audit, string normalisation, and date-by-category pivot on them using used_mb as the field?”
Dataset group storage pivot report
“Can you pivot my storage data by date and dataset_group category and also flag any quality or string issues? I'll send you up to 200 rows with fields: row_id, dataset_group (like 'group-a' or 'group-b'), date, used_mb, and row_count.”
Row count anomaly and normalisation audit
“Please audit the quality and normalise string fields in my storage records and give me a date-by-category breakdown — I want to use row_count as the analysis field across my dataset groups.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical passes — quality auditing, string normalisation, and categorical date pivoting — on a batch of up to 200 dataset storage records in a single $0.01 call. It is best suited for data engineering pipelines that need quick, offline assessment of storage metrics (used_mb, row_count) without any external enrichment or live data lookups. Prefer alternatives if you need more than 200 rows, real-time data, or analytics on fields beyond used_mb and row_count.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, used_mb, or row_count) — validation error
- Invalid date format (not YYYY-MM-DD) — schema pattern validation failure
- field parameter set to a value other than 'used_mb' or 'row_count' — enum validation error
- Payment of 0.01 USDC not completed — request blocked by x402 payment requirement
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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