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Dataset Storage Analytics — Record Quality Audit, String Normalisation & IQR Outlier Flags
x402
Dataset Storage Analytics — Record Quality Audit, String Normalisation & 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, string normalisation, and IQR-based outlier flagging — up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + 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 for up to 200 supplied dataset storage rows: (1) a record quality audit highlighting missing values, type mismatches, and row-level quality issues; (2) string normalisation results for text fields such as dataset_group; and (3) IQR-based outlier flags identifying statistically anomalous values in either used_mb or row_count depending on the selected field.
Example use cases
Storage usage anomaly detection
“I have 50 rows of daily dataset storage records with used_mb and row_count values — can you flag any IQR outliers in the used_mb field and tell me which rows look suspicious?”
Pre-pipeline data quality gate
“Before I load these 120 dataset storage records into our pipeline, can you run a full quality audit on them and normalize the dataset_group strings so the labels are consistent?”
Monthly storage dataset review
“Run a record quality audit plus IQR outlier check on row_count for these dataset storage entries covering October 2026 — I want to catch any bad rows before the monthly report goes out.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined data quality operations — quality audit, string normalisation, and IQR outlier detection — on dataset storage records in a single low-cost call. It is ideal for pre-pipeline data validation, anomaly detection on storage metrics, or cleaning batch exports of up to 200 rows without requiring external lookups, live data, or professional data services.
Side effects
- Exceeding 200 rows causes a validation error — batch must be split
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) cause schema rejection
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside allowed range (-1000000000000 to 1000000000000) are rejected
- Too few rows may produce unreliable IQR outlier detection (IQR needs sufficient spread)
- Payment failure via x402 prevents execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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