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Dataset Storage Analytics — Record Quality Audit, String Normalisation & Secondary-Measure Weighted Mean
x402
Dataset Storage Analytics — Record Quality Audit, String Normalisation & Secondary-Measure Weighted Mean 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 bundled analytical reports on up to 200 dataset storage records: record quality audit, string normalisation, and secondary-measure weighted mean for used_mb or row_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean 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: (1) a record quality audit flagging missing, out-of-range, or malformed values; (2) a string normalisation report on dataset_group and other string fields; and (3) a weighted mean of the selected numeric measure (used_mb or row_count) using the secondary measure as weights — all computed locally without external lookups.
Example use cases
Storage usage quality audit
“I have a batch of dataset storage records with fields like dataset_group, date, used_mb, and row_count — can you audit them for quality issues, normalise the group name strings, and give me a weighted mean of used_mb across all 150 rows?”
Row count analytics across groups
“Run the three analytics reports on my dataset storage data — quality audit, string normalisation, and weighted mean — but use row_count as the measure field instead of used_mb. I'll paste in about 80 rows with row_id, dataset_group, date, used_mb, and row_count.”
Detecting dirty group labels in storage data
“I suspect some of my dataset_group labels are inconsistently formatted across my storage records. Can you run a string normalisation report along with the quality audit and weighted mean on these 60 rows so I can see what needs cleaning?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled data quality and statistical reports on dataset storage records in a single low-cost call — specifically when you want record quality auditing, string normalisation, and a weighted mean computation together without making external API calls or requiring live data lookup. Ideal for batch analytical pipelines processing up to 200 storage usage rows at a time.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) causes rejection
- Date values not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside allowed range trigger validation errors
- Invalid field enum value (not 'used_mb' or 'row_count') causes parameter error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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