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Null Coverage Analytics — Record Quality Audit, String Normalisation & Adjacent Row Changes
x402
Null Coverage Analytics — Record Quality Audit, String Normalisation & Adjacent Row Changes 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).
Runs three independent analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 rows of null-coverage data containing missing_values and total_values fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes 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: (1) a record quality audit summarising completeness and validity issues across supplied rows, (2) a string normalisation report identifying and standardising inconsistencies in text fields such as dataset_group, and (3) an adjacent row changes report highlighting value shifts between consecutive rows for the selected field (missing_values or total_values). All analysis is performed locally on supplied data with no external lookups.
Example use cases
Dataset completeness audit for ETL pipeline
“I have 120 rows from our ETL pipeline with row IDs, dataset group names, dates in YYYY-MM-DD format, and missing_values and total_values counts. Can you run a record quality audit, string normalisation check, and adjacent row changes report on the missing_values field for all of them?”
Daily null-coverage drift detection
“Here are 80 daily null-coverage records across two dataset groups — 'group-a' and 'group-b' — with missing and total value counts. Run the adjacent row changes and record quality audit reports on the total_values field so I can see if anything shifted significantly day over day.”
String consistency check across dataset groups
“I've got 60 null-coverage rows with dataset group labels that might have inconsistent capitalisation or formatting. Can you run the string normalisation report alongside the record quality audit on my missing_values data so I can clean it up before loading into the warehouse?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, cost-effective analytical pass over null-coverage records — specifically when you want all three of record quality auditing, string normalisation, and adjacent row change detection in a single $0.01 call on up to 200 rows. Prefer it over building custom scripts when you want reproducible, engine-consistent reports across dataset groups without any external API calls, live lookups, or professional assessments involved.
Side effects
- Exceeding 200 rows causes rejection — trim input to at most 200 rows
- Missing required fields (row_id, dataset_group, date, missing_values, total_values) causes validation failure
- Dates not matching YYYY-MM-DD pattern are rejected
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array (minItems:1 not met) causes schema error
- Payment failure via x402 protocol blocks execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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