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Dataset Storage Analytics: Record Quality Audit, Exact Row Deduplication & Base-100 Series
x402
Dataset Storage Analytics: Record Quality Audit, Exact Row Deduplication & 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 2026-10-15).
Runs three analytical reports on supplied dataset storage records (used_mb, row_count): a record quality audit, exact row deduplication, and a base-100 index series — all locally, with no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + Exact row deduplication + Base-100 series: Record quality audit + Exact row deduplication + 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: (1) a record quality audit highlighting missing values, anomalies, or invalid entries in the supplied rows; (2) an exact row deduplication report identifying identical records; and (3) a base-100 indexed series for the selected numeric field (used_mb or row_count), starting from the specified base row index. All computation is local with no external data fetched.
Example use cases
Pre-pipeline data quality check
“Before I load this batch of 150 storage records into our warehouse, can you audit their quality, flag any exact duplicates, and give me a base-100 series on used_mb starting from the first row? I'll paste the rows with row_id, dataset_group, date, used_mb, and row_count.”
Monthly storage deduplication review
“I've got my monthly storage usage export — about 80 rows across dataset groups A and B. Can you run an exact row deduplication check plus a record quality audit on them, and also index the row_count field as a base-100 series starting at position 0?”
Storage trend indexing for reporting
“I need to turn these 60 dataset storage records into a base-100 indexed series on used_mb so I can compare trends across groups — and while you're at it, please also audit record quality and check for duplicate rows. Here are the rows with dates, dataset groups, and storage values.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-218?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three analytical operations — quality audit, deduplication, and base-100 indexing — on the same dataset storage records in a single call for a flat $0.01 USDC. It is ideal for pre-ingestion data validation pipelines, periodic storage reporting, or trend normalization tasks on batches up to 200 rows. Prefer alternatives if you need live data retrieval, predictive analytics, professional assessment, or datasets larger than 200 rows.
Side effects
- Submitting more than 200 rows returns a validation error — batch must be split
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) causes schema validation failure
- base_index out of range (>= number of submitted rows) causes an indexing error
- Non-conforming date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric fields outside allowed range cause schema rejection
- Empty rows array (minItems: 1 not met) returns a validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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