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Dataset Storage Record Quality Audit and String Normalisation
x402
Dataset Storage Record Quality Audit and String Normalisation 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 data quality and normalises strings for up to 200 dataset storage records (used_mb, row_count), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied dataset storage records (used_mb, row_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two independent reports: (1) a record quality audit highlighting issues such as nulls, out-of-range values, and anomalies in used_mb and row_count fields; (2) a string normalisation report for text fields (row_id, dataset_group). Both reports are generated locally without any external lookups or predictions.
Example use cases
Storage pipeline pre-ingestion quality check
“Before I load these 150 dataset storage records into our warehouse, can you audit their quality and normalise the string fields? Each row has a row_id, dataset_group, date, used_mb, and row_count.”
Detecting nulls and bad values in storage logs
“I have a batch of storage usage logs with used_mb and row_count values — some might be null or out of range. Can you run a quality audit on them and also normalise the dataset_group strings?”
Cleaning dataset group names before reporting
“My storage records have inconsistent capitalisation and spacing in the dataset_group field. Can you normalise those strings and also flag any quality issues in the used_mb and row_count columns across all my rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-001?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a data quality audit and string normalisation for dataset storage records (used_mb, row_count) in a single call. It is ideal for pre-ingestion checks, data pipeline validation, and cleaning storage logs where you want two independent analytical reports without any external API calls or live lookups. Best suited for batches up to 200 rows.
Side effects
- More than 200 rows submitted — request rejected due to row limit
- Missing required fields (row_id, dataset_group, date, used_mb, or row_count) cause validation errors
- Malformed date strings not matching YYYY-MM-DD pattern rejected
- Empty rows array rejected (minItems: 1)
- Numeric values outside allowed range cause schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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