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Feature Availability Analytics: Adjacent Row Changes + Explicit Missing-Value Fill
x402
Feature Availability Analytics: Adjacent Row Changes + Explicit Missing-Value Fill 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 to produce two reports: adjacent row change detection and explicit missing-value fill, using local analytical engines on up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill 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 analytical reports: (1) an adjacent row change report identifying shifts between consecutive rows in the specified field, and (2) a missing-value fill report showing imputed values using the chosen method (zero, mean, or median) — all computed locally on the supplied records with no external lookups.
Example use cases
Daily feature drift monitoring
“Here are 150 rows of feature availability logs by date and group — run the adjacent row changes report on populated_features so I can see where counts jumped or dropped between consecutive days.”
Missing value imputation for pipeline audit
“I've got a dataset of expected_features and populated_features records with some nulls — fill the missing populated_features values using mean imputation and give me both the fill report and the adjacent row change report.”
Cross-group feature completeness review
“Can you analyze these 80 feature availability records across group-a and group-b — I need the adjacent row change detection and a zero-fill for any missing expected_features values so I can audit completeness?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-162?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both adjacent row change detection and missing-value imputation on feature availability data in a single call, with no need for external lookups or predictions. It is well-suited for data pipeline audits, feature engineering QA, and time-series completeness checks on small to medium datasets (up to 200 rows). Prefer this over general-purpose analytics when your data conforms to the populated_features/expected_features schema and you want two bundled reports at low cost.
Side effects
- Input array exceeds 200 rows — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- Unknown fill method value (not zero/mean/median) — enum validation failure
- Unknown field value (not populated_features/expected_features) — enum validation failure
- Empty rows array (minItems:1 violated) — validation error
- row_id missing or empty string — required field error
- Numeric values out of allowed range (±1 trillion) — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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