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Feature Availability Analytics — Quality Audit, Deduplication & Grouped Summary
x402
Feature Availability Analytics — Quality Audit, Deduplication & Grouped Summary 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: a record quality audit, exact row deduplication, and a grouped numeric summary of populated_features or expected_features values.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Exact row deduplication + Grouped numeric summary: Record quality audit + Exact row deduplication + Grouped numeric summary 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 nulls, format errors, and data issues; (2) an exact row deduplication report identifying and removing duplicate records; and (3) a grouped numeric summary (e.g. sum, mean, count) of the selected field (populated_features or expected_features) broken down by dataset_group and/or date. All computed locally with no external calls.
Example use cases
Feature completeness audit before ML pipeline
“I have a batch of feature availability records with populated_features and expected_features columns. Can you run a record quality audit plus exact row deduplication and then give me a grouped numeric summary by dataset_group? I want to make sure the data is clean before feeding it into my ML pipeline. Focus the summary on populated_features.”
Detecting duplicate feature tracking entries
“We're getting suspect duplicate entries in our feature tracking table. Can you take these rows — each has a row_id, dataset_group, date, populated_features, and expected_features — and run exact deduplication along with the quality audit and a grouped summary on expected_features to see what's going on?”
Summarizing feature fill rates by dataset group
“I need a quick grouped numeric summary of how populated_features varies across my dataset groups, plus a quality audit to flag any bad records and a dedup check. Here are about 150 feature availability rows with dates ranging through October 2026 — can you process them?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-211?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of quality audit, exact deduplication, and grouped numeric summarization on feature availability data in a single $0.01 call (up to 200 rows), without requiring any external data lookups, live APIs, or prediction services. It is ideal for pre-processing feature store records, validating ML training data, or auditing feature tracking logs entirely offline.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) causes row-level rejection
- Invalid date format (non-YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside ±1,000,000,000,000 boundary are rejected
- Empty rows array returns an error
- Invalid field enum value (not populated_features or expected_features) causes schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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