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Null Coverage Record Quality Audit and Missing Value Fill
x402
Null Coverage Record Quality Audit and Missing Value Fill 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 coverage in row data and fills missing values (missing_values or total_values fields) using zero, mean, or median imputation for up to 200 rows, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill for supplied null coverage records (missing_values, total_values). 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
Returns two independent analytical reports: (1) a record quality audit covering null coverage statistics across the supplied rows, and (2) an explicit missing-value fill report showing imputed values for the specified field using the chosen method (zero, mean, or median). All computation is performed locally with no external lookups.
Example use cases
Data pipeline null coverage check
“I have a batch of 10 dataset rows across two groups and I need a quality audit of their null coverage plus median imputation on the missing_values field — can you run both reports? The rows are: row-1, group-alpha, 2026-11-01, 5 missing, 100 total; row-2, group-beta, 2026-11-02, null missing, 200 total; row-3, group-alpha, 2026-11-03, 8 missing, 150 total.”
Zero-fill missing totals for reporting
“Some of my total_values entries are null and I need them filled with zero for a report I'm preparing. I have rows like: row-A, group-sales, 2026-09-01, 12 missing, null total; row-B, group-sales, 2026-09-02, 7 missing, 300 total — can you audit the record quality and apply zero-fill on total_values?”
Mean imputation across dataset groups
“I need to impute missing_values using mean imputation for my dataset quality audit — I've got rows spread across two groups: row-x1, group-east, 2026-08-15, null missing, 500 total; row-x2, group-west, 2026-08-16, 40 missing, 600 total; row-x3, group-east, 2026-08-17, null missing, 450 total. Give me both the audit report and the filled values.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-019?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a null coverage quality audit and missing value imputation in a single paid call for up to 200 rows of structured dataset records. Ideal for offline or batch data quality workflows where no live data lookup or external prediction is required, and where two bundled analytical reports (audit + fill) are needed at low cost ($0.01 USDC).
Side effects
- Rows array is empty or missing — validation error
- Row count exceeds 200 — request rejected
- missing_values or total_values out of allowed numeric range — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Unsupported method enum value — validation error
- Unsupported field enum value — validation error
- Payment not completed — 402 response blocking execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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