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Feature Availability Analytics — Quality Audit, Deduplication & Numeric Row Ordering
x402
Feature Availability Analytics — Quality Audit, Deduplication & Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs three local analytical reports on feature availability records: record quality audit, exact row deduplication, and numeric row ordering — all in a single call for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Exact row deduplication + Numeric row ordering: Record quality audit + Exact row deduplication + Numeric row ordering for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports: (1) a record quality audit flagging data issues across the supplied rows, (2) an exact row deduplication report identifying and removing duplicate records, and (3) a numerically ordered listing of rows ranked by the selected numeric field (populated_features or expected_features). All computed locally without external lookups.
Example use cases
Feature completeness audit for ML pipeline
“I have a batch of feature availability records from my ML pipeline — each row has a row_id, dataset_group, date, populated_features count, and expected_features count. Can you run a quality audit and flag any data issues, plus deduplicate them and order them by populated_features?”
Deduplication check on dataset group records
“We've been collecting feature availability logs across dataset groups like 'group-a' and 'group-b' and I suspect there are duplicate rows. Can you deduplicate these records and also give me a quality audit and numeric ordering by expected_features for the full set?”
Pre-analysis data cleaning for feature store
“Before I load these feature availability records into our feature store, I need to clean them up — please run a quality audit, remove exact duplicates, and sort them numerically by populated_features so I can review the output.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-213?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a bundled, cost-efficient single-call solution for three common data hygiene tasks — quality auditing, deduplication, and numeric ordering — specifically on feature availability records with populated_features and expected_features fields. Ideal for pre-processing steps before loading data into feature stores or ML pipelines, where paying $0.01 for all three reports at once is more efficient than calling separate endpoints.
Side effects
- Fewer than 1 row provided — minimum 1 row required
- More than 200 rows submitted — maxItems limit exceeded
- Invalid date format — must match YYYY-MM-DD pattern
- row_id missing or empty — required field
- populated_features or expected_features outside allowed numeric range
- Numeric values provided as strings instead of numbers or null
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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