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Time Series Completeness — Record Quality Audit, String Normalisation & Exact Row Deduplication
x402
Time Series Completeness — Record Quality Audit, String Normalisation & Exact Row Deduplication 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 time series completeness records (observed_points vs expected_points) for quality issues, normalises strings, and identifies exact duplicate rows across up to 200 rows, returning 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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 delivered together: (1) a record quality audit flagging issues in the time series completeness data, (2) a string normalisation report showing standardised field values, and (3) an exact row deduplication report identifying identical rows — all computed locally with no external lookups.
Example use cases
Daily completeness check for ETL pipelines
“I have 50 rows of daily ETL completeness logs with observed_points and expected_points per dataset_group and date — can you run a record quality audit, normalise any messy strings, and flag exact duplicate rows so I can trust the data going into my dashboard?”
Deduplicating multi-source sensor data
“We're merging completeness records from two sensor feeds and suspect there are exact duplicate rows. Can you audit the data quality, normalise the group names, and give me a deduplication report for these 120 rows with observed and expected point counts per day?”
Validating completeness metrics before reporting
“Before I send this time series completeness report to the client, can you run a quality audit on these 80 rows — checking observed_points vs expected_points across our dataset groups — and also normalise the strings and remove any exact duplicate records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-191?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, cost-effective ($0.01 USDC for all 3 reports) local analysis of time series completeness records specifically tracking observed vs expected data points — especially when you want quality audit, string normalisation, and exact deduplication in one call without any live lookups or external integrations.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) causes rejection
- Invalid date format (not YYYY-MM-DD) triggers a schema error
- Values outside the allowed numeric range (-1,000,000,000,000 to 1,000,000,000,000) are rejected
- Empty rows array is not accepted
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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