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Time Series Completeness — Duplicate Key Report + Adjacent Row Changes
x402
Time Series Completeness — Duplicate Key Report + 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 2026-10-28).
Runs two analytical reports on time series completeness data: a duplicate key detection report and an adjacent row change analysis, processing up to 200 rows of observed vs. expected data points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Duplicate key report + Adjacent row changes: Duplicate key report + Adjacent row changes 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
Returns two independent reports: (1) a duplicate key report identifying rows with repeated key combinations within the dataset, and (2) an adjacent row changes report showing value differences between consecutive rows for the selected field (observed_points or expected_points). Results cover up to 200 input rows and are computed locally without external lookups.
Example use cases
Auditing sensor data completeness logs
“I have daily completeness records for my IoT sensors — each row has a row_id, dataset_group, date, observed_points, and expected_points. Can you run the duplicate key report and adjacent row changes analysis on up to 150 rows using the observed_points field so I can spot data gaps and repeated entries?”
Validating ETL pipeline output integrity
“After our nightly ETL job, I need to verify the completeness records it produced — around 80 rows with row_id, dataset_group, date, observed_points, and expected_points. Run the duplicate key and adjacent row change reports on the expected_points field to check if anything looks off.”
Monitoring content delivery metric completeness
“We track daily content delivery completeness across multiple groups and I want to check for duplicate keys and sequential fluctuations in our observed_points across 120 rows — each has a row_id, dataset_group, date, observed_points, and expected_points. Can you run both analytical reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-062?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both duplicate key detection and adjacent row change analysis on time series completeness data (observed vs. expected point counts) in a single cost-efficient call. It is best suited for data quality audits of completeness logs across dated, grouped records where you want to spot key collisions and sequential value shifts without any external data enrichment or live lookups.
Side effects
- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, observed_points, or expected_points) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Null values in the selected field may produce incomplete adjacent change calculations
- Empty rows array (minItems: 1 not met) causes rejection
- Numeric values outside allowed range (-1T to +1T) fail schema validation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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