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Feature Availability — String Normalisation + Adjacent Row Changes
x402
Feature Availability — String Normalisation + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Analyzes feature availability records by performing string normalisation and detecting adjacent row changes across up to 200 rows, producing 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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
Two independent analytical reports: (1) a string normalisation report standardizing the feature availability values across submitted rows, and (2) an adjacent row changes report identifying where consecutive rows differ in the selected feature field (populated_features or expected_features). Results cover up to 200 submitted rows with no external data sources.
Example use cases
Feature completeness audit by dataset group
“I have 50 rows of feature availability records across two dataset groups — 'train-set' and 'test-set' — with dates from 2025-01-01 to 2025-02-19. Can you run string normalisation and detect adjacent row changes on the populated_features field to help me understand where feature counts shift day-over-day?”
Expected vs populated feature gap detection
“My ML pipeline tracks expected_features and populated_features for each row daily. Can you analyze these 120 rows of availability data and generate both the string normalisation and adjacent row change reports on expected_features so I can see where things deviate?”
Data quality check before model training
“Before I kick off model training I want to sanity-check my feature availability table — I've got 200 rows with dataset_group, date, populated_features, and expected_features columns. Run the string normalisation and adjacent row changes analysis on populated_features and tell me what you find.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-029?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local (no external lookup) analytical computation on feature availability records — specifically when you need both string normalisation and adjacent row change detection in a single call for up to 200 rows. Ideal for ML pipeline data quality checks, feature store audits, and dataset completeness monitoring where you want two distinct reports from one low-cost API call.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Null values in both populated_features and expected_features — may produce incomplete report
- Invalid field enum value (must be 'populated_features' or 'expected_features') — error
- Empty rows array — minimum 1 item required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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