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Feature Availability — Adjacent Row Changes + IQR Outlier Flags
x402
Feature Availability — 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 2026-10-20).
Analyzes feature availability records (populated_features, expected_features) to detect adjacent row changes and flag IQR-based outliers across up to 200 rows, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied feature availability records (populated_features, expected_features). 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 for the submitted rows: (1) an adjacent row changes report showing delta values between consecutive rows for the selected field, and (2) an IQR outlier flags report identifying rows whose field values fall outside the interquartile range bounds. Both reports reference rows by row_id and date, enabling precise identification of anomalous or shifted records.
Example use cases
Daily feature pipeline anomaly detection
“I have 30 days of feature availability logs for my ML pipeline — each row has a date, dataset group, and counts of populated vs expected features. Can you run adjacent row change detection and IQR outlier flagging on the populated_features field to spot any days where something went wrong?”
Dataset completeness audit across groups
“Here are 150 rows of feature availability records across two dataset groups, 'group-a' and 'group-b', going back 75 days each. Run both the adjacent row changes report and the IQR outlier flags on expected_features so I can see which dates had unusual completeness.”
Catching data ingestion spikes in feature store
“My feature store ingestion has been flaky — I've got 60 rows with daily populated_features and expected_features counts. Can you analyze them for sudden adjacent-row jumps and IQR-based outliers on populated_features so I can identify the problematic ingestion days?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-157?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have time-series or sequentially ordered feature availability records and need both temporal change detection (adjacent row deltas) and statistical outlier identification (IQR method) in a single call. It is ideal for data quality monitoring pipelines, ML feature store audits, and dataset completeness checks where you want two complementary analytical perspectives for one low flat fee of $0.01 USDC without any external data lookups or predictions.
Side effects
- Fewer than 1 row submitted — request rejected as minItems is 1
- More than 200 rows submitted — request rejected as maxItems is 200
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) — validation failure
- Non-numeric values in populated_features or expected_features — type validation error
- Field selector not in enum ['populated_features','expected_features'] — validation error
- Insufficient data spread for meaningful IQR (e.g. all identical values) — may produce trivial or empty outlier flags
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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