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Null Coverage Analytics — String Normalisation & Date-by-Category Pivot
x402
Null Coverage Analytics — 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).
Analyzes null coverage records by running string normalization and a date-by-category pivot report on up to 200 rows of missing/total value data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — String normalisation + Date by category pivot: String normalisation + 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 for the submitted batch (up to 200 rows): (1) a string normalization report cleaning and standardizing string fields, and (2) a date-by-category pivot showing the selected metric (missing_values or total_values) broken down by dataset_group across dates. All computation is local with no external lookups or predictions.
Example use cases
Data quality audit by group
“I have a CSV of null coverage records for 150 rows across groups 'sales', 'marketing', and 'ops' with dates in October 2026. Can you pivot the missing_values by dataset_group and date, and also normalize the string fields so I can see which groups are worst for data completeness?”
Monthly completeness pivot for pipeline
“Run a date-by-category pivot on my total_values field across these 80 null coverage records — the groups are 'users', 'transactions', and 'events' with dates from 2026-09-01 to 2026-09-30. I also need the string normalization report for the same batch.”
Spot-check missing values across two teams
“I've got 40 null coverage rows split between dataset groups 'team-alpha' and 'team-beta' across Q3 2026. Can you give me a string normalization report and a missing_values pivot by category and date so I can present a data quality summary to my manager?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, local batch analytics on null coverage data — specifically when you want both a string normalization pass and a date-by-category pivot in a single low-cost call ($0.01 USDC). Ideal for data quality pipelines, completeness audits, or reporting workflows that already have structured records with missing_values and total_values. Not suitable when you need live data lookups, predictive modeling, or more than 200 rows per call.
Side effects
- Batch exceeds 200 rows — request is rejected; split into smaller batches
- Missing required fields (row_id, dataset_group, date, missing_values, total_values) — validation error returned
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Null or out-of-range numeric values for missing_values or total_values beyond ±1,000,000,000,000 — rejected
- Empty rows array — minimum 1 item required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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