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Null Coverage Analytics — Record Quality Audit, String Normalisation & IQR Outlier Flags
x402
Null Coverage 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 2026-10-14).
Audits null/missing-value coverage records by performing record quality checks, string normalisation, and IQR-based outlier flagging across up to 200 rows in three independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied null coverage records (missing_values, total_values). 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 the submitted rows: (1) a record quality audit highlighting completeness and integrity issues, (2) a string normalisation report standardising text fields like dataset_group, and (3) IQR-based outlier flags identifying anomalous values in the selected numeric field (missing_values or total_values). Covers up to 200 input rows per call.
Example use cases
Data pipeline null rate anomaly detection
“I have a batch of up to 150 rows from my ETL pipeline, each with a row ID, dataset group name, date, and counts for missing and total values — can you flag any outliers in the missing_values field using IQR and also give me a quality audit and string normalisation report?”
Monthly data completeness review
“Run a quality audit on my November completeness records — I have about 80 rows with dataset_group labels, dates in YYYY-MM-DD format, and missing_values vs total_values counts. I want all three reports: quality audit, normalised strings, and IQR outlier flags on the total_values field.”
Cross-group null coverage comparison
“I'm comparing null coverage across three dataset groups over Q3 — here are 200 rows with row IDs, group names, dates, and missing vs total value counts. Can you give me the record quality audit, string normalisation, and IQR-based outlier detection on the missing_values column?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need simultaneous record quality auditing, string normalisation, and IQR outlier detection on null/missing-value coverage data in a single low-cost call. It is purpose-built for datasets with missing_values and total_values columns grouped by dataset_group and date. Prefer it over generic data quality tools when your data is already structured in this schema and you want three bundled analytical perspectives for $0.01 USDC without any external lookups or predictions.
Side effects
- More than 200 rows submitted — exceeds maxItems limit, request rejected
- missing required fields (row_id, date, missing_values, total_values) — row validation fails
- date not in YYYY-MM-DD format — pattern validation error
- numeric values outside ±1,000,000,000,000 range — schema rejection
- empty rows array — minItems:1 violated
- invalid field enum value other than 'missing_values' or 'total_values' — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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