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Time Series Completeness Audit — Record Quality, String Normalisation & Numeric Row Ordering
x402
Time Series Completeness Audit — Record Quality, String Normalisation & Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Audits up to 200 time-series completeness records (observed vs. expected points) across three reports: record quality audit, string normalisation, and numeric row ordering — all processed locally with no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering 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 for the submitted batch (up to 200 rows): (1) a record quality audit identifying malformed or problematic entries, (2) a string normalisation report for dataset_group and related string fields, and (3) a numeric row ordering report ranking rows by the selected field (observed_points or expected_points). All processing is local with no external calls.
Example use cases
Daily pipeline completeness health check
“I have 120 rows of daily completeness records from our data pipeline — each row has a date, dataset group, observed points, and expected points. Can you run a full record quality audit plus string normalisation and numeric ordering on the observed_points field so I can spot gaps or bad data?”
Audit survey dataset before reporting
“Before I submit this week's survey data report, can you validate my 80 completeness records for data quality issues, normalise the dataset_group strings, and order everything by expected_points? Here are the rows with date, row_id, dataset_group, observed_points, and expected_points.”
Normalise and sort sensor coverage records
“I've got 200 rows of sensor coverage completeness logs — row_id, date, dataset_group, observed_points, and expected_points — and I need all three analytics reports: quality audit, string normalisation on the group names, and numeric ordering of the expected_points column.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-196?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined, low-cost ($0.01 USDC) batch audit of time series completeness records covering data quality, string cleanliness, and numeric ordering in a single call — ideal for pre-reporting validation of observed vs. expected point datasets of up to 200 rows where no live prediction or external enrichment is required.
Side effects
- Batch exceeds 200 rows — request rejected with validation error
- Missing required fields (row_id, date, observed_points, expected_points) — row rejected or error returned
- Invalid date format (not YYYY-MM-DD) — validation failure
- Null numeric values may affect ordering report completeness
- Unsupported field enum value — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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