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Time Series Completeness Analytics — Record Quality Audit, Deduplication & Numeric Distribution
x402
Time Series Completeness Analytics — Record Quality Audit, Deduplication & 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 2026-10-15).
Runs three local analytical reports on time series completeness records: a record quality audit, exact row deduplication, and numeric distribution analysis for observed_points and expected_points fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + Exact row deduplication + Numeric distribution: Record quality audit + Exact row deduplication + Numeric distribution 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
Returns three independent analytical reports: (1) a record quality audit flagging null values, malformed fields, and data integrity issues; (2) an exact row deduplication report identifying duplicate records; and (3) a numeric distribution summary (e.g. min, max, mean, percentiles) for the selected numeric field (observed_points or expected_points). All analysis is performed locally on the supplied rows with no external lookups.
Example use cases
Data pipeline completeness health check
“I have 80 rows of daily completeness records for my ETL pipeline — each row has a date, a dataset group name, observed_points, and expected_points. Can you run the quality audit, deduplication, and numeric distribution reports on them so I can see where the gaps and anomalies are?”
Detecting duplicate completeness entries
“My monitoring system has been logging completeness records and I suspect there are exact duplicate rows in the last 3 months of data. Can you run the deduplication report on these 120 rows, along with the quality audit and distribution analysis for the observed_points field?”
Statistical profiling for completeness QA review
“We're doing a QA review of our data completeness logs — about 150 rows with observed_points and expected_points across several dataset groups. Can you give me the record quality audit, exact row deduplication check, and a numeric distribution breakdown for the expected_points field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-210?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — quality audit, deduplication, and numeric distribution — on a small-to-medium batch of time series completeness records (up to 200 rows) in a single $0.01 call. Ideal when you want offline, local analysis with no external lookups or side effects, and when your data involves observed vs. expected point counts across dates and dataset groups.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) cause schema rejection
- Malformed date strings not matching YYYY-MM-DD pattern are rejected
- Null numeric values are handled but may affect distribution statistics
- Empty rows array is rejected (minItems: 1)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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