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Null Coverage Analytics: Record Quality Audit + String Normalisation + Date-by-Category Pivot
x402
Null Coverage Analytics: Record Quality Audit + String Normalisation + 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 2026-10-18).
Runs three analytical reports on supplied null-coverage data (missing_values, total_values per row): a record quality audit, string normalisation, and a date-by-category pivot — up to 200 rows, all offline.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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
Three structured analytical reports returned in a single response: (1) a record quality audit summarising null coverage per row, (2) a string normalisation report cleaning up dataset_group labels, and (3) a date-by-category pivot aggregating the chosen field (missing_values or total_values) by date and group — all computed locally with no external lookups.
Example use cases
Dataset completeness audit by group
“I've got a table of dataset records with missing_values and total_values for each row, grouped into 'sales' and 'inventory' categories across dates in October 2026 — run the record quality audit report on missing_values so I can see which groups have the worst completeness.”
Normalise group labels before pipeline ingestion
“Before I push my null-coverage data into the pipeline, can you run the string normalisation report on my dataset_group field? I have about 150 rows covering dates from 2026-09-01 to 2026-09-30 with missing_values and total_values filled in.”
Pivot null counts by date and category
“I need to understand how missing values trend over time across my dataset groups — take my 80 rows (with row_id, dataset_group, date, missing_values, total_values) and produce the date-by-category pivot report so I can spot which dates and groups are worst.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of null-coverage data (missing_values + total_values per row with group and date labels) and need all three analytical passes — quality audit, string normalisation, and date-category pivot — in a single low-cost call. Prefer it over building custom scripts when you want offline, reproducible local analytics with no external data dependencies and a fixed $0.01 price for the full bundle.
Side effects
- Fewer than 1 row or more than 200 rows supplied — request rejected
- Row missing required fields (row_id, dataset_group, date, missing_values, total_values) — validation error
- date field not in YYYY-MM-DD format — pattern validation failure
- field enum value not 'missing_values' or 'total_values' — enum rejection
- numeric values outside ±1,000,000,000,000 bounds — schema error
- Payment of 0.01 USDC not completed — 402 response returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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