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Coordinate Quality Checks — Adjacent Row Changes + IQR Outlier Flags
x402
Coordinate Quality Checks — Adjacent Row Changes + 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 2026-11-02).
Analyzes up to 200 rows of coordinate quality check records, producing two independent reports: adjacent row change detection and IQR-based outlier flagging for checked_points or flagged_points fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Coordinate quality checks — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied coordinate quality checks records (checked_points, flagged_points). 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
Two independent analytical reports: (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 bounds — both computed locally on the supplied data with no external lookups.
Example use cases
Spike detection in daily QC runs
“I have daily coordinate quality check records for two station groups over the past month — can you run adjacent row change analysis and IQR outlier detection on the checked_points field to find any days with suspicious spikes or sudden jumps?”
Audit flagged points for anomalies
“Here are 150 rows of coordinate quality check data with station groups and dates — please analyze the flagged_points field using IQR outlier flagging and adjacent row change reports so I can see which records are statistically unusual.”
Pre-release data sanity check
“Before I publish this coordinate quality dataset, run an IQR outlier check and adjacent row change analysis on the checked_points column across all 80 rows — I want to catch any bad values before they go out.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-23-157?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular coordinate quality check records (checked_points, flagged_points) and need both adjacent row change detection and IQR-based outlier flagging in a single low-cost call. It is well-suited for batch audits of up to 200 rows where no live data lookup or external enrichment is needed — just local statistical analysis on your supplied data. Prefer it over general-purpose analytics when your data specifically tracks coordinate quality metrics by station group and date.
Side effects
- Fewer than 1 row supplied — minimum row count not met
- More than 200 rows supplied — exceeds maximum batch size
- Missing required fields (row_id, station_group, date, checked_points, or flagged_points) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Null values in the selected field may reduce statistical reliability of IQR computation
- Invalid field enum value (must be 'checked_points' or 'flagged_points') — rejected input
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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