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Time Series Completeness — Record Quality Audit + String Normalisation + Duplicate Key Report
x402
Time Series Completeness — Record Quality Audit + String Normalisation + Duplicate Key Report 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 up to 200 time series completeness records for data quality issues, normalises strings, and identifies duplicate keys across three independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report 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: (1) a record quality audit highlighting issues in the supplied time series completeness rows, (2) a string normalisation report showing standardised versions of string fields like dataset_group, and (3) a duplicate key report identifying rows with repeated row_id values. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Data pipeline completeness audit
“I have 50 rows of time series completeness data from my ETL pipeline — each row has a row_id, dataset_group, date, observed_points, and expected_points. Can you run a full quality audit, normalise the string fields, and flag any duplicate keys?”
Detecting duplicate keys before merge
“Before I merge these time series completeness records into our warehouse, can you run a duplicate key report on the 120 rows I'm supplying? Each row has a row_id, dataset_group like 'sensor-west', a date in YYYY-MM-DD format, and observed and expected point counts.”
String normalisation for group labels
“My dataset has inconsistent capitalisation and formatting in the dataset_group field across 80 time series completeness rows. Can you normalise those strings and also give me a record quality audit and duplicate report while you're at it?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-192?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined, cost-effective batch audit of time series completeness records covering data quality, string normalisation, and duplicate detection in a single call. Best suited for datasets of up to 200 rows where no live data lookup, prediction, or external enrichment is needed — just local analytical processing of supplied observed_points and expected_points data.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) cause row-level failures
- Invalid date format (non-YYYY-MM-DD) triggers pattern validation error
- observed_points or expected_points outside ±1,000,000,000,000 range rejected
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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