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Time Series Completeness — Record Quality Audit + String Normalisation
x402
Time Series Completeness — Record Quality Audit + String Normalisation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Audits record quality and normalises strings for supplied time series completeness data (observed_points, expected_points) across up to 200 rows, producing 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation: Record quality audit + String normalisation 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: (1) a record quality audit flagging issues such as missing values, out-of-range numbers, or malformed dates in the completeness rows, and (2) a string normalisation report covering dataset_group and row_id field formatting. Both reports cover up to 200 supplied rows and are generated using local analytical engines with no external lookups.
Example use cases
ETL pipeline completeness check
“I have daily completeness records for two dataset groups — 'sensor-east' and 'sensor-west' — covering the past 5 days with observed and expected point counts. Can you run a record quality audit and string normalisation on all 10 rows and give me the two reports?”
Data warehouse coverage audit
“Audit the quality of these 50 time series completeness records for our data warehouse, split across groups 'dw-core' and 'dw-archive', with dates from 2025-01-01 to 2025-02-19 and their observed vs expected point counts — I need both the record quality and string normalisation reports.”
Monitoring dashboard data validation
“Before I load these completeness metrics into our monitoring dashboard, can you run a quality audit and string normalisation check on these 30 rows? They cover groups 'api-calls' and 'batch-jobs' with daily observed_points and expected_points for March 2025.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-001?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw time series completeness records (observed vs expected point counts) and need both a structured data quality audit and string normalisation without any live data lookups, external predictions, or professional assessments. Ideal for lightweight, low-cost ($0.01 USDC) batch validation of up to 200 rows within an automated data pipeline or ETL workflow.
Side effects
- Submitting more than 200 rows returns a validation error
- Rows missing required fields (row_id, date, observed_points, expected_points) are rejected or flagged
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Null or out-of-range numeric values may produce incomplete audit results
- Malformed JSON body returns a 400-level error
- Payment failure via x402 prevents processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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