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Time Series Completeness Analyzer — Record Quality Audit + Deduplication + Adjacent Row Changes
x402
Time Series Completeness Analyzer — Record Quality Audit + Deduplication + Adjacent Row Changes 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).
Runs three analytical reports on time series completeness records: record quality audit, exact row deduplication, and adjacent row changes detection — all locally, up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + Exact row deduplication + Adjacent row changes: Record quality audit + Exact row deduplication + Adjacent row changes 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 rows: (1) a record quality audit flagging malformed or problematic records, (2) an exact row deduplication report identifying duplicate entries, and (3) an adjacent row changes report showing value differences between consecutive rows — all computed locally without external lookups.
Example use cases
Pre-pipeline data quality gate
“Before I load this batch of time series completeness records into our warehouse, can you run a full quality audit, deduplication check, and adjacent row change analysis on these 80 rows? Each row has a date, dataset_group, row_id, observed_points, and expected_points — analyze the observed_points field.”
Detecting duplicate completeness logs
“I have 150 rows of daily completeness tracking data across several dataset groups and I suspect some rows got logged twice. Can you run exact deduplication plus the record quality audit on these rows? Fields are row_id, dataset_group, date, observed_points, and expected_points.”
Monitoring day-over-day completeness shifts
“I need to see how my expected_points values are shifting day over day across these completeness records — run the adjacent row changes report along with the quality audit for these 40 rows grouped by dataset_group and date.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-217?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical operations — quality audit, deduplication, and adjacent-row-change detection — on time series completeness data (observed vs expected points) in a single $0.01 call, without requiring any external data lookups or live predictions. Ideal for data engineering pipelines validating completeness logs before ingestion, or for monitoring dataset groups day-over-day.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) causes row-level rejection
- Invalid date format (non YYYY-MM-DD) fails pattern validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array is rejected (minItems: 1)
- Payment failure via x402 protocol prevents execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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