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Schema Conformity Adjacent Row Changes and IQR Outlier Flags
x402
Schema Conformity Adjacent Row Changes and 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 2026-10-18).
Analyzes schema conformity time-series data (valid_records, checked_records) to detect adjacent row changes and flag IQR-based outliers across up to 200 rows in two independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Schema conformity — Adjacent row changes + IQR outlier flags: Adjacent row changes + 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 per call: (1) an adjacent row change report showing differences between consecutive rows for the selected field, and (2) an IQR outlier flag report identifying rows whose values fall outside the interquartile range boundary. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Daily data pipeline quality monitoring
“Here are 120 rows of daily schema conformity records for our ETL pipeline with valid_records and checked_records per date — run adjacent row change detection and IQR outlier flags on valid_records so I can see where data quality dropped unexpectedly.”
Audit trail for compliance dataset validation
“I need to audit our compliance dataset group's conformity history — I have 80 rows with dates and valid_records counts. Can you detect adjacent row changes and flag any IQR outliers on the checked_records field?”
Spot anomalies before weekly QA report
“Before I send out the weekly QA report, can you check these 40 rows of schema conformity records across two dataset groups and flag any sudden row-to-row changes or IQR outliers in valid_records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-02-157?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local statistical analysis of schema conformity time-series data specifically — combining adjacent row delta detection with IQR-based outlier flagging in a single call. Prefer it over general-purpose analytics when your data already has the valid_records/checked_records schema conformity structure and you want two complementary anomaly views at low cost ($0.01 for both reports).
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, date, valid_records, checked_records) on any row — validation error
- Date format not matching YYYY-MM-DD pattern — schema rejection
- Invalid field enum value other than valid_records or checked_records — rejected
- All values null — IQR computation may be undefined or empty
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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