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Dataset Storage Record Quality Audit, String Normalisation & Exact Row Deduplication
x402
Dataset Storage Record Quality Audit, String Normalisation & Exact Row Deduplication 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).
Audits quality, normalises strings, and deduplicates rows in dataset storage records (used_mb, row_count) — three analytical reports per call, up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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 covering: (1) a record quality audit identifying missing, null, or out-of-range values in used_mb and row_count fields; (2) a string normalisation report flagging inconsistencies in dataset_group and other string fields; and (3) an exact row deduplication report identifying fully duplicate rows across the supplied dataset.
Example use cases
Pre-pipeline storage data cleanup
“I'm about to load our dataset storage logs into a pipeline and I want to make sure they're clean first — can you audit the record quality, normalise any inconsistent strings in the dataset_group field, and flag any exact duplicate rows? Here are the records with used_mb and row_count values.”
Monthly storage usage deduplication
“At the end of each month we export our storage usage table and there are often duplicate rows. Can you run exact deduplication plus a quality audit on this batch of up to 150 storage records, each with a date, row_id, dataset_group, used_mb, and row_count?”
Data quality report for storage datasets
“I need a three-part report on these dataset storage records — record quality audit, string normalisation issues, and exact row duplicates. The rows have fields for row_id, dataset_group, date, used_mb, and row_count. Can you process all 80 of them?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-191?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined, low-cost ($0.01 USDC) batch analysis of dataset storage records covering all three dimensions — quality, string consistency, and exact deduplication — without needing live lookups, predictions, or external data enrichment. Ideal for pre-pipeline data preparation or periodic storage table hygiene checks on batches up to 200 rows.
Side effects
- More than 200 rows submitted — request rejected at schema validation
- Missing required fields (row_id, dataset_group, date, used_mb, or row_count) on any row — validation error
- date field not matching YYYY-MM-DD pattern — schema rejection
- used_mb or row_count outside allowed numeric range — schema rejection
- Empty rows array — minItems violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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