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Feature Availability Analytics: Record Quality Audit + String Normalisation + Grouped Numeric Summary
x402
Feature Availability Analytics: Record Quality Audit + String Normalisation + Grouped Numeric Summary is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs three local analytical reports on feature availability records: a record quality audit, string normalisation, and a grouped numeric summary — all within a single $0.01 USDC call for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + 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 missing, null, or malformed values across the submitted rows; (2) string-normalised versions of string fields (e.g. dataset_group); and (3) a grouped numeric summary (e.g. counts, min, max, mean) for the selected field (populated_features or expected_features) broken down by dataset_group. All computed locally with no external lookups.
Example use cases
Feature coverage completeness audit
“I have 120 feature availability records with populated_features and expected_features counts per dataset group. Can you run a record quality audit on them, normalise the dataset_group strings, and give me a grouped numeric summary so I can see which groups have the worst feature coverage?”
Pre-release dataset health check
“Before we release this dataset, can you check the quality of all 80 feature availability rows — flag any bad records, normalise the string fields, and summarise the expected_features numbers grouped by dataset_group?”
Daily feature pipeline monitoring
“Every morning I want to audit today's feature availability batch — about 200 rows — for record quality issues, normalise the string fields, and produce a grouped numeric summary on populated_features so I can spot anomalies by group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-194?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, low-cost ($0.01 USDC) triple-report on feature availability data covering quality auditing, string normalisation, and grouped numeric summarisation in one call, without requiring any external API lookups, ML predictions, or real-time data sources. Ideal for data pipeline hygiene checks on datasets of up to 200 rows where offline, deterministic analytics are sufficient.
Side effects
- More than 200 rows submitted — request rejected with a row-limit error
- Missing required fields (row_id, date, populated_features, expected_features) cause validation failure
- Date strings not matching YYYY-MM-DD pattern are flagged or rejected
- Null numeric values may be excluded or flagged in the quality audit
- Invalid field selector enum value returns a schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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