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Schema Conformity Analytics — Record Quality Audit, String Normalisation & Base-100 Series
x402
Schema Conformity 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 analytical reports (record quality audit, string normalisation, and base-100 rebase series) on up to 200 rows of schema conformity data containing valid_records and checked_records fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Schema conformity — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series for supplied schema conformity records (valid_records, checked_records). 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 delivered together: (1) a record quality audit summarising completeness and anomalies in valid_records and checked_records, (2) a string normalisation report cleaning and standardising text fields like dataset_group, and (3) a base-100 index series rebasing the chosen numeric field relative to the specified base row — all computed locally without external lookups.
Example use cases
Daily data pipeline quality review
“I have a week of schema conformity logs across two dataset groups — can you audit the record quality, normalise the string fields, and generate a base-100 series using valid_records starting from the first row? Here are 7 rows with row_id, dataset_group, date, valid_records, and checked_records.”
Benchmarking conformity across groups
“Run the record quality audit and base-100 rebase report on my 50-row conformity dataset using checked_records as the field and row index 5 as the base, so I can compare how each dataset group is trending relative to that reference point.”
Monthly schema validation summary
“I need a string normalisation and quality audit on our monthly schema conformity export — 120 rows with date, dataset_group, valid_records, and checked_records — can you generate all three reports and tell me where the data quality looks weakest?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-02-201?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined conformity analytics reports in a single $0.01 call on up to 200 rows of schema validation data. It is ideal for batch auditing of dataset quality logs where valid_records and checked_records are the key metrics, and you want quality audit, string normalisation, and a base-100 trend series without any external data lookups or live predictions.
Side effects
- More than 200 rows submitted — endpoint rejects input exceeding maxItems limit
- Missing required fields (row_id, date, valid_records, checked_records) causes validation error
- Date not matching YYYY-MM-DD pattern results in schema rejection
- base_index out of range (0–199) causes parameter error
- Null values in numeric fields may affect base-100 calculation accuracy
- Invalid field enum value (not valid_records or checked_records) returns error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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