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Time Series Completeness — Record Quality Audit + Exact Row Deduplication + Numeric Row Ordering
x402
Time Series Completeness — Record Quality Audit + Exact Row Deduplication + Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs three independent analytical reports (record quality audit, exact row deduplication, and numeric row ordering) on time series completeness data containing observed and expected point counts, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + Exact row deduplication + Numeric row ordering: Record quality audit + Exact row deduplication + Numeric row ordering for supplied time series completeness records (observed_points, expected_points). 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 for the submitted rows: (1) a record quality audit flagging malformed or anomalous entries, (2) an exact row deduplication report identifying and flagging duplicate rows, and (3) a numeric row ordering report ranking rows by the selected numeric field (observed_points or expected_points). All computed locally without external lookups.
Example use cases
Daily sensor data completeness audit
“I have a batch of daily sensor records with observed and expected data point counts for each dataset group. Can you audit them for quality issues, find any exact duplicate rows, and sort them by observed_points? Here are up to 100 rows with row_id, dataset_group, date, observed_points, and expected_points.”
Pipeline completeness deduplication check
“My data pipeline is producing time series completeness logs and I suspect there are duplicate entries. Can you run exact row deduplication, a quality audit, and a numeric ordering report on these records using the expected_points field?”
Data warehouse completeness validation
“Before I load these completeness records into the warehouse, I want to validate them — can you audit record quality, remove exact duplicates, and order the rows numerically by observed_points? I'll send you up to 200 rows with date, row_id, dataset_group, observed_points, and expected_points.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-213?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical operations — quality audit, deduplication, and numeric ordering — applied to time series completeness records in a single $0.01 call, without requiring any external data lookup or live prediction. It is best suited for offline/batch validation of datasets up to 200 rows where observed_points and expected_points are the key numeric dimensions.
Side effects
- Exceeding 200 rows returns an error — batch must be split
- Missing required fields (row_id, date, observed_points, expected_points) cause validation failure
- Date strings not matching YYYY-MM-DD pattern are rejected
- Numeric values outside the allowed range (-1e12 to 1e12) cause schema rejection
- Invalid field enum value (anything other than observed_points or expected_points) returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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