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Dataset Storage Analytics: Record Quality Audit + String Normalisation + Base-100 Series
x402
Dataset Storage Analytics: Record Quality Audit + String Normalisation + Base-100 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 analytical reports on up to 200 dataset storage records — a record quality audit, string normalisation, and a base-100 index series — for supplied used_mb and row_count data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series 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 for the supplied dataset storage rows: (1) a record quality audit flagging completeness and anomalies, (2) a string normalisation report for text fields like dataset_group, and (3) a base-100 index series for the chosen numeric field (used_mb or row_count) anchored at the specified base_index row. All computed locally with no external lookups.
Example use cases
Storage usage quality audit
“I have a list of 50 dataset storage records with dates, used_mb, and row_count values — can you audit them for quality issues, normalise the dataset_group strings, and produce a base-100 index series on used_mb starting from the first row?”
Row count trend indexing
“Can you take my 30 storage dataset rows and generate a base-100 series on the row_count field starting from index 0, plus run a quality audit and string normalisation on the dataset_group names?”
Multi-group dataset validation
“I've got storage records for two dataset groups over the past quarter — about 80 rows total with used_mb and row_count — please audit them for record quality, normalise the string fields, and compute a base-100 series on used_mb from the beginning.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-201?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 dataset storage records and need all three of a quality audit, string normalisation, and base-100 series in a single low-cost call ($0.01 USDC). It is a self-contained, local analytical service — ideal when you do not need live data lookups, predictions, or external enrichment, and want a bundled multi-report output for storage metrics (used_mb, row_count) across named dataset groups.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, dataset_group, used_mb, or row_count) — validation error
- date field does not match YYYY-MM-DD pattern — schema validation failure
- base_index out of range for the supplied row count — index error
- All null values in the selected numeric field — base-100 series cannot be computed
- Malformed JSON body — request parse error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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