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Feature Availability Analytics — Record Quality Audit, Exact Row Deduplication, and Numeric Distribution
x402
Feature Availability Analytics — Record Quality Audit, Exact Row Deduplication, and Numeric Distribution 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 data: a record quality audit, exact row deduplication, and numeric distribution analysis for populated_features or expected_features.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Exact row deduplication + Numeric distribution: Record quality audit + Exact row deduplication + Numeric distribution 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 reports: (1) a record quality audit flagging null, invalid, or malformed values in the submitted feature availability rows; (2) an exact row deduplication report identifying any fully duplicate rows; (3) a numeric distribution analysis (e.g. min, max, mean, percentiles, histogram) for either the populated_features or expected_features column as specified. All processing is done locally with no external lookups.
Example use cases
Feature completeness QA before model training
“I have a batch of feature availability records for two dataset groups across October 2026 — can you audit their quality, flag any exact duplicate rows, and show me the numeric distribution of populated_features so I know if the data is ready for model training?”
Detecting duplicate entries in feature logs
“My feature tracking pipeline may have logged duplicate entries — can you run an exact row deduplication check on these 150 feature availability records with row_id, date, dataset_group, populated_features, and expected_features fields to find any redundant rows?”
Numeric spread analysis of expected feature counts
“I want to understand how expected_features values are distributed across my dataset — can you run a numeric distribution report on these feature availability records grouped by dataset_group and date?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-210?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three complementary data quality reports — quality audit, deduplication, and numeric distribution — on feature availability records in a single low-cost call. Ideal when data is already in hand (no live lookup needed), batch size is under 200 rows, and you want offline, deterministic analytics without external dependencies. Prefer over single-report endpoints when you need all three analyses simultaneously for the same dataset.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, dataset_group, populated_features, expected_features) causes row-level rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Numeric values outside the allowed range trigger input rejection
- Empty rows array returns an error
- Payment not included or insufficient USDC causes 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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