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Time Series Completeness — Record Quality Audit + Adjacent Row Changes
x402
Time Series Completeness — Record Quality Audit + Adjacent Row Changes 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).
Audits time series completeness records for data quality issues and computes adjacent row changes across up to 200 rows, delivering two independent analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + Adjacent row changes: Record quality audit + Adjacent row changes for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two independent analytical reports per call: (1) a record quality audit flagging issues in the supplied time series completeness rows, and (2) an adjacent row changes report showing differences between consecutive observations for the selected field (observed_points or expected_points). Results are based entirely on the supplied data with no external lookups.
Example use cases
Daily pipeline completeness audit
“I have 30 days of completeness records for my ETL pipeline — each row has a date, a dataset group, and observed vs expected data points. Can you audit the record quality and show me how observed_points change between adjacent rows?”
Sensor data gap detection
“I'm tracking sensor readings over the past month and want to know if there are quality issues in my completeness logs and how the expected_points shift day by day — I have about 90 rows across two sensor groups.”
Reporting dataset validation
“Before I publish my weekly report, can you check the quality of these 60 completeness records across three dataset groups and flag any anomalies or big swings between adjacent rows in the expected_points field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-011?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two bundled analytical reports — record quality audit plus adjacent row change detection — on time series completeness data in a single low-cost call. It is ideal for batch offline analysis of up to 200 rows where no live data lookup, prediction, or external enrichment is needed. Prefer this over single-report endpoints when both quality auditing and row-to-row change analysis are simultaneously required.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) causes rejection
- Malformed date strings not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside the allowed range trigger errors
- Empty rows array is rejected due to minItems:1 constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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