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Numeric Precision Comparisons — Record Quality Audit + Trailing Mean Series
x402
Numeric Precision Comparisons — Record Quality Audit + 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).
Performs two analytical reports on supplied numeric pairs (source_value, transformed_value): a record quality audit and a trailing mean series, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Numeric precision comparisons — Record quality audit + Trailing mean series: Record quality audit + Trailing mean series for supplied numeric precision comparisons records (source_value, transformed_value). 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
Two independent analytical reports: (1) a record quality audit flagging issues at the row level for each source_value/transformed_value pair, and (2) a trailing mean series for the selected field (source_value or transformed_value) over the specified rolling window — both computed locally with no external lookups.
Example use cases
ETL pipeline transformation validation
“I have 80 rows from our ETL pipeline with original and transformed sales figures — can you run a record quality audit and a trailing mean series with a window of 7 on the source_value to check if our transformation is introducing precision errors?”
Rolling average for financial data review
“We have 150 daily records of source and transformed revenue values across two business units — please compute the trailing mean series with a window of 10 on the transformed_value field and include the record quality audit so we can spot any bad rows.”
Data quality check before model training
“Before we train our model, can you audit these 40 rows of source_value and transformed_value pairs for record quality and also give me a trailing mean series with window size 3 on source_value? The rows span October 2026 and are grouped under 'group-a' and 'group-b'.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-20-010?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a record-level data quality audit and a rolling mean time series computed together on a batch of up to 200 numeric comparison rows — especially for validating ETL transformations or numeric precision pipelines without any external data lookups or live predictions. It is cost-effective at $0.01 USDC for both reports simultaneously.
Side effects
- Fewer than 1 row or more than 200 rows supplied causes rejection
- Missing required fields (row_id, date, dataset_group, source_value, transformed_value) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Window size outside 2–200 range is rejected
- Numeric values outside ±1,000,000,000,000 are rejected
- Payment of 0.01 USDC not included causes authorization failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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