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Feature Availability Analytics: Record Quality Audit + String Normalisation + Category Frequencies
x402
Feature Availability Analytics: Record Quality Audit + String Normalisation + Category Frequencies 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 (record quality audit, string normalisation, and category frequencies) on up to 200 feature availability records containing populated and expected feature counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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 for the supplied feature availability rows: (1) a record quality audit flagging completeness and consistency issues, (2) string normalisation results for text fields like dataset_group, and (3) category frequency counts summarising distribution across groups and dates — all computed locally with no external lookups.
Example use cases
Feature completeness audit for ML pipeline
“I have a batch of feature availability records from our ML pipeline — up to 50 rows with row IDs, dataset group names, dates, and counts of populated vs expected features. Can you audit the record quality, normalise the dataset group strings, and show me the category frequencies all at once?”
Data observability check across dataset groups
“We track how many features get populated versus how many are expected each day across three dataset groups. Can you analyse my 120 rows of feature availability data and give me a quality audit, normalise any inconsistent group name strings, and break down the category frequencies?”
Pre-release data validation for feature store
“Before we push our feature store update, I need to validate 80 rows of feature availability records — checking record quality, normalising the string labels in our dataset_group column, and getting a frequency summary of which categories appear most. Can you run all three reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three data quality reports (quality audit, string normalisation, category frequencies) on feature availability records in a single cost-efficient call. Ideal when working with ML feature stores or data pipelines where you want to check feature completeness and group distributions offline, without external lookups or predictions. Best suited for batches of 1–200 rows where all three report types are needed together.
Side effects
- Fewer than 1 row submitted — minimum row count not met
- More than 200 rows submitted — exceeds batch limit
- Missing required fields (row_id, date, populated_features, expected_features) in one or more rows
- Date field does not match YYYY-MM-DD pattern — validation error
- populated_features or expected_features values outside allowed numeric range
- row_id exceeds 80 character limit
- Payment of 0.01 USDC not completed — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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