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Null Coverage Record Quality Audit + Date by Category Pivot
x402
Null Coverage Record Quality Audit + Date by Category Pivot 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).
Analyzes supplied row data to produce two analytical reports: a null coverage/record quality audit and a date-by-category pivot, using local engines on up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Record quality audit + Date by category pivot: Record quality audit + Date by category pivot 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 analytical reports: (1) a record quality audit showing null coverage metrics (missing vs total values, rates) per row or group, and (2) a date-by-category pivot summarizing missing and total values broken down by date and dataset_group. Both reports are computed locally from the supplied rows with no external lookups.
Example use cases
Dataset completeness audit before pipeline run
“Before I kick off my ETL pipeline, can you audit this batch of 150 rows for null coverage? Each row has a row_id, dataset_group, date, missing_values, and total_values — I want to know the record quality and see a pivot of missing_values by date and group.”
Weekly data quality report by category
“Every week I get a CSV with up to 200 rows of group-level data including missing_values and total_values per date. Can you generate a null coverage audit and a date-by-category pivot so I can see which groups are degrading in quality over time?”
Compare null rates across two dataset groups
“I have two dataset groups — 'group-a' and 'group-b' — with rows spanning October 2026, each with missing_values and total_values counts. Can you run both a record quality audit and a date pivot so I can compare null coverage between the two groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-017?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular row data with explicit missing_values and total_values counts and need two complementary analytical views — a per-record null quality audit AND a date-by-category pivot — in a single low-cost call. It is ideal for batch data quality checks on up to 200 rows where no external data lookup, prediction, or live enrichment is needed.
Side effects
- Fewer than 1 row supplied — minimum 1 row required
- More than 200 rows submitted — maximum cap exceeded, request rejected
- Row missing required fields (row_id, dataset_group, date, missing_values, total_values)
- Date field not matching YYYY-MM-DD pattern — validation error
- missing_values or total_values outside allowed numeric range
- row_id exceeds 80 character limit
- field enum value not one of: missing_values, total_values
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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