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Label Review Analytics: Adjacent Row Changes + IQR Outlier Flags
x402
Label Review Analytics: 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 2026-10-24).
Analyzes label review time-series data to detect adjacent row-to-row changes and flag IQR-based outliers across reviewed_records or changed_labels fields, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied label review records (reviewed_records, changed_labels). 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) an adjacent row change report showing delta values between consecutive rows for the selected field, and (2) an IQR outlier flag report identifying which rows fall outside the interquartile range threshold — both scoped to the supplied dataset of up to 200 rows, with no external lookups or predictions.
Example use cases
Audit annotation pipeline for data drift
“I have 120 rows of daily label review logs across two dataset groups with columns for date, reviewed_records, and changed_labels — can you run the adjacent row change and IQR outlier analysis on changed_labels to spot any suspicious spikes in our annotation pipeline?”
Quality check before ML model retraining
“Before we kick off retraining, I need to check our last 90 days of label review records for anomalies — please run the IQR outlier flags and adjacent row change report on the reviewed_records field so I can sanity-check the training data quality.”
Investigate sudden drop in review throughput
“Our label review throughput looked weird last month — I've got the daily records with row IDs, dates, and dataset groups, can you analyze the reviewed_records field for adjacent row changes and IQR outliers to pinpoint when things went off the rails?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-157?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, local statistical analysis on label review tabular data — specifically adjacent row change detection and IQR-based outlier flagging — without any external API calls, ML predictions, or professional assessments. Ideal for MLOps and data quality workflows where you want two fast, reproducible analytical reports in a single $0.01 call on up to 200 rows.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, reviewed_records, or changed_labels) — validation error returned
- Date fields not matching YYYY-MM-DD pattern — schema validation failure
- Invalid field selector value (must be 'reviewed_records' or 'changed_labels') — enum validation error
- All-null numeric values for the selected field — analysis may produce empty or degenerate output
- Payment of 0.01 USDC not completed — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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