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Schema Conformity Record Quality Audit with IQR Outlier Flags
x402
Schema Conformity Record Quality Audit with 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 2026-10-17).
Performs a two-report quality audit on schema conformity records (valid_records, checked_records), including record quality analysis and IQR-based outlier detection, for up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Schema conformity — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags 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 summarizing the health and completeness of the supplied schema conformity rows, and (2) IQR-based outlier flags identifying rows where the selected field (valid_records or checked_records) deviates significantly from the interquartile range. No external lookups or predictions are performed — all analysis is based solely on the supplied data.
Example use cases
Data pipeline conformity review
“I have up to 200 rows of schema conformity data from my ETL pipeline, each with a date, group name, valid_records, and checked_records — can you run a quality audit and flag any IQR outliers in the valid_records field?”
Detect anomalous checked record counts
“Here's my dataset of checked_records and valid_records grouped by dataset group and date — I want a record quality report plus any rows flagged as outliers based on IQR, analyzing the checked_records field specifically.”
Weekly data quality health check
“Every week I track how many records pass schema validation across my groups. Can you audit this batch of up to 150 rows for record quality issues and highlight any valid_records values that look like outliers using IQR analysis?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-02-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of up to 200 schema conformity records and need both a holistic data quality audit and statistical outlier detection (IQR-based) in a single low-cost call. Prefer this over generic analytics APIs when your data specifically tracks valid_records and checked_records by date and group, and you want two structured reports without any external data enrichment or predictions.
Side effects
- Fewer than 1 row supplied — minimum row count not met
- More than 200 rows submitted — exceeds maximum batch size
- Missing required fields (row_id, date, valid_records, checked_records) on any row
- Date field not matching YYYY-MM-DD pattern
- Field selector not one of 'valid_records' or 'checked_records'
- Null values in both numeric fields making IQR computation impossible
- Insufficient data variance for meaningful IQR outlier detection (all values identical)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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