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Dataset Storage Analytics: Record Quality Audit + Exact Row Deduplication + IQR Outlier Flags
x402
Dataset Storage Analytics: Record Quality Audit + Exact Row Deduplication + IQR Outlier Flags 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 (used_mb, row_count): a record quality audit, exact row deduplication check, and IQR-based outlier flagging — all from supplied data, no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + Exact row deduplication + IQR outlier flags: Record quality audit + Exact row deduplication + IQR outlier flags 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 identifying missing, malformed, or invalid fields; (2) an exact row deduplication report listing identical rows; and (3) IQR-based outlier flags for the selected numeric field (used_mb or row_count). All analysis is performed locally on the supplied data with no external data access.
Example use cases
Storage usage anomaly detection
“I have a list of up to 150 dataset storage records with row IDs, group names, dates, used_mb, and row_count values. Can you flag any IQR outliers in the used_mb field and tell me which rows look suspicious?”
Duplicate row audit before ETL pipeline
“Before I run my ETL pipeline, can you check these 80 dataset storage records for exact duplicate rows and give me a deduplication report? The records have row_id, dataset_group, date, used_mb, and row_count columns.”
Full data quality check on row_count metrics
“Run a full quality audit on my dataset storage records — I want quality issues, duplicates, and outlier flags all in one go, focused on the row_count field. I have about 60 rows with dataset_group, date, used_mb, and row_count.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-220?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 quality, deduplication, and outlier detection in a single call — especially for datasets up to 200 rows where no live data lookup or external enrichment is needed. It is ideal for pre-pipeline data validation, storage metric audits, and lightweight data hygiene checks on used_mb or row_count fields.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, used_mb, or row_count) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Numeric values outside allowed range trigger schema errors
- Empty rows array returns an error
- Payment not included or incorrect amount causes 402 rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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