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Null Coverage Analytics: Record Quality Audit + String Normalisation + Base-100 Series
x402
Null Coverage Analytics: Record Quality Audit + String Normalisation + Base-100 Series 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 local analytical reports (record quality audit, string normalisation, and base-100 index series) on supplied null-coverage data (missing_values and total_values) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series 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 independent analytical reports for the supplied null-coverage records: (1) a record quality audit flagging anomalies in missing/total value fields, (2) a string normalisation report cleaning dataset_group and row_id labels, and (3) a base-100 indexed series for the chosen field relative to the specified base row — all computed locally without external lookups or predictions.
Example use cases
Monthly data completeness audit
“I need a record quality audit, string normalisation, and base-100 series for my October null-coverage data — 3 rows: row-1 in group-alpha on 2026-10-01 with 5 missing out of 100 total, row-2 in group-beta on 2026-10-02 with 12 missing out of 100 total, row-3 in group-alpha on 2026-10-03 with 0 missing out of 100 total. Analyse the missing_values field starting from base index 0.”
Cross-group null coverage comparison
“Run all three null coverage reports on my two dataset groups: row-A in group-sales on 2026-11-01 had 200 missing out of 1000 total, row-B in group-marketing on 2026-11-01 had 50 missing out of 800 total, row-C in group-ops on 2026-11-01 had 0 missing out of 600 total. Use total_values as the field and base index 0.”
Time-series completeness indexing
“Can you compute the base-100 index series, quality audit, and string normalisation for my weekly null-coverage readings? Five rows: row-w1 in group-main on 2026-09-01 with 8 missing of 200, row-w2 on 2026-09-08 with 15 missing of 200, row-w3 on 2026-09-15 with 3 missing of 200, row-w4 on 2026-09-22 with 22 missing of 200, row-w5 on 2026-09-29 with 10 missing of 200. Field is missing_values, base index 2.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-201?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined, low-cost bundle of three data-quality analytics (quality audit, string normalisation, base-100 series) on null-coverage records without needing external data lookups, predictions, or live integrations. It is best suited for offline or batch data quality checks on small-to-medium datasets (up to 200 rows) where all data is already available as missing_values and total_values pairs.
Side effects
- Validation error if rows array is empty or exceeds 200 items
- Schema violation if row objects are missing required fields (row_id, dataset_group, date, missing_values, total_values)
- Invalid date format if date strings do not match YYYY-MM-DD pattern
- base_index out of range if it exceeds the number of submitted rows minus one
- Numeric overflow if missing_values or total_values exceed the 1,000,000,000,000 limit
- Payment failure if 0.01 USDC x402 payment is not attached
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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