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Sampling Observations — Duplicate Key Report + Trailing Mean Series
x402
Sampling Observations — Duplicate Key Report + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Analyzes up to 200 sampling observation rows to produce a duplicate key report and a trailing (rolling) mean series for a chosen numeric field.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Duplicate key report + Trailing mean series: Duplicate key report + Trailing mean series for supplied sampling observations records (sample_value, sample_weight). 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 row_id or key combinations, and (2) a trailing (rolling) mean series computed over the chosen field (sample_value or sample_weight) for the specified window size, covering all supplied rows up to 200.
Example use cases
Data quality audit before analysis
“I have 120 sampling records with row_id, dataset_group, date, sample_value, and sample_weight — can you run a duplicate key check on them and also give me a trailing mean over sample_value with a window of 10?”
Rolling trend smoothing for experiment results
“Here are 80 experiment sample observations. Please compute a trailing mean series for the sample_weight field using a window of 4, and also flag any duplicate row IDs you find.”
Survey sample deduplication and trend check
“I've got 200 survey sample rows and I need two things: a report showing any duplicate row IDs or dataset_group keys, and a rolling mean series over sample_value with window size 7. Can you run both?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-061?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both duplicate key detection and rolling mean computation on the same set of sampling observations in a single call, saving cost versus two separate requests. Ideal for data quality checks combined with trend smoothing on small-to-medium datasets (up to 200 rows) without needing live lookups, predictions, or external data enrichment.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause validation errors
- Window size outside range 2–200 results in an error
- Dates not matching YYYY-MM-DD pattern cause schema rejection
- All null values in the target field may produce empty or undefined mean series
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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