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Schema Conformity — Record Quality Audit + Missing-Value Fill
x402
Schema Conformity — Record Quality Audit + Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-17).
Audits data quality of schema conformity records (valid_records, checked_records) and fills missing values using zero, mean, or median imputation — two independent reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Schema conformity — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill for supplied schema conformity records (valid_records, checked_records). 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 independent analytical reports: (1) a record quality audit identifying problematic, missing, or out-of-range values across the submitted rows, and (2) a missing-value fill report showing the original and imputed values for the chosen field (valid_records or checked_records) using the selected method (zero, mean, or median). Results are computed locally with no external lookups.
Example use cases
ETL pipeline conformity gap detection
“I just pulled my nightly ETL run results — can you audit these 50 schema conformity rows for data quality issues and fill any missing valid_records values using the mean? Here are the rows with row_id, dataset_group, date, valid_records, and checked_records.”
Median imputation for checked_records nulls
“My dataset has about 80 conformity tracking rows and several checked_records values are null. Run a quality audit and fill those gaps using median imputation so I can proceed with my analysis.”
Zero-fill missing conformity counts by group
“I have conformity records across two dataset groups spanning three months — some valid_records entries are missing. Audit the record quality and backfill all nulls with zero so my downstream reporting stays consistent.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-02-019?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a data quality audit and missing-value imputation for schema conformity metrics (valid_records, checked_records) in a single low-cost call. It is ideal for batch offline analysis of up to 200 rows where no live data fetching, machine learning prediction, or external enrichment is needed. Prefer it over general-purpose data tools when you specifically track schema validation pass rates and need reproducible, locally computed imputation.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, dataset_group) causes row rejection
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation error
- Values outside ±1,000,000,000,000 are rejected by schema
- Invalid method enum value (not zero/mean/median) causes a bad request error
- Empty rows array (minItems:1 violated) returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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