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Schema Conformity — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Schema Conformity — Record Quality Audit + String Normalisation + IQR Outlier Flags 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 on schema conformity data: a record quality audit, string normalisation, and IQR-based outlier flagging for valid_records and checked_records fields across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Schema conformity — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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
Returns three independent analytical reports for the submitted schema conformity rows: (1) a record quality audit identifying completeness and consistency issues, (2) a string normalisation report cleaning up text fields, and (3) IQR-based outlier flags marking statistically anomalous values in the chosen numeric field (valid_records or checked_records). All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Data pipeline conformity health check
“I have 80 rows of daily schema conformity data with valid_records and checked_records counts across two dataset groups. Can you run a record quality audit, normalise the strings, and flag any IQR outliers in the valid_records field so I can spot which days had anomalous conformity?”
Outlier detection in checked records
“My team tracks checked_records daily across several dataset groups — I have about 120 rows going back four months. Run the IQR outlier detection on the checked_records field and include the record quality audit and string normalisation reports so I get the full picture.”
Ad-hoc data quality review before reporting
“Before I send this conformity report to stakeholders, can you audit these 40 rows of schema conformity data for quality issues, normalise any inconsistent strings in the dataset_group column, and flag any outliers in valid_records using the IQR method?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-02-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled set of three data quality reports — record quality audit, string normalisation, and IQR outlier detection — on schema conformity metrics (valid_records, checked_records) for up to 200 rows at a flat cost. It is ideal for batch data quality checks on conformity tracking datasets where no live data lookup, prediction, or external action is needed. Prefer sibling endpoints if you need different analytical combinations such as daily sum series, histograms, or pivot tables.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, valid_records, checked_records) cause row-level rejection
- Malformed date strings not matching YYYY-MM-DD pattern are rejected
- Numeric values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) are rejected
- Invalid field enum value (anything other than valid_records or checked_records) causes an error
- Empty rows array is not accepted (minItems: 1)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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