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Feature Availability Analytics: Record Quality Audit + Exact Row Deduplication + Base-100 Series
x402
Feature Availability Analytics: Record Quality Audit + Exact Row Deduplication + Base-100 Series 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 analytical reports on feature availability records: record quality audit, exact row deduplication, and a base-100 index series — all computed locally against supplied data, no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Exact row deduplication + Base-100 series: Record quality audit + Exact row deduplication + Base-100 series 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
Returns three independent analytical reports computed locally against the supplied rows: (1) a record quality audit flagging nulls, anomalies, or incomplete records; (2) an exact row deduplication report identifying identical records; and (3) a base-100 index series normalizing the chosen numeric field (populated_features or expected_features) relative to the specified base row index. No external data is fetched.
Example use cases
ML feature store data quality check
“I've got a batch of feature availability records from our ML feature store — about 150 rows with populated_features and expected_features counts per dataset_group and date. Can you run the quality audit and deduplication check on them, using populated_features for the base-100 series starting at index 0?”
Detecting duplicate feature log entries
“We suspect our feature availability log has duplicate entries. Can you run exact row deduplication on these records — they have row_id, dataset_group, date, populated_features, and expected_features columns — along with the quality audit and base-100 series on expected_features from index 0?”
Normalizing feature counts over time
“I want to normalize our expected_features counts into a base-100 series across dates for these dataset groups. Can you run all three reports on my 80-row feature availability dataset using expected_features as the field and base index 0?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-218?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need simultaneous data quality auditing, deduplication detection, and base-100 normalization on feature availability records in a single low-cost call. It is ideal for offline/batch data QA workflows on ML feature store logs or similar tabular datasets with populated vs expected feature counts, when no live external lookup or prediction is needed and all analysis is self-contained on supplied data.
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds maximum batch size
- Invalid date format (non YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, populated_features, expected_features) — validation error
- base_index out of range (>= number of rows) — computation error
- Null values in both populated_features and expected_features may degrade index series computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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