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Dataset Storage Analytics: Record Quality Audit + String Normalisation + Category Frequencies
x402
Dataset Storage Analytics: Record Quality Audit + String Normalisation + Category Frequencies 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 dataset storage records — record quality audit, string normalisation, and category frequency analysis — for up to 200 rows of used_mb and row_count data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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 nulls, invalid values, and anomalies in used_mb and row_count fields; (2) a string normalisation report cleaning and standardising text fields like dataset_group; (3) a category frequency table showing counts and distributions across dataset_group values. All derived solely from the supplied rows — no external lookups or live data.
Example use cases
Storage usage data quality check
“I have 120 rows of storage usage data with fields like row_id, dataset_group, date, used_mb, and row_count — can you audit them for quality issues like missing values or out-of-range numbers, normalise the string fields, and tell me how often each dataset_group appears?”
Monthly dataset group frequency report
“Here are our monthly storage records across dataset groups like 'prod', 'staging', and 'archive' — run category frequency analysis plus a quality audit and string normalisation on all 80 rows so I can see which groups dominate usage.”
Pre-migration data cleansing check
“Before we migrate these 200 dataset storage records to the new warehouse, can you do a record quality audit to flag bad data, normalise the dataset_group strings, and give me a frequency breakdown by category?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined, low-cost batch analysis of dataset storage records covering data quality, string cleanliness, and categorical distribution in one call. It is best suited for offline or near-real-time data auditing workflows where you already hold the row data locally and need structured analytical reports without any external API calls, predictions, or professional assessments.
Side effects
- Exceeds 200-row limit — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) — validation error
- Invalid date format (must be YYYY-MM-DD) — parsing error
- Numeric fields outside allowed range (±1,000,000,000,000) — validation rejection
- Empty rows array — error response
- Payment not included or insufficient — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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