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Numeric Precision Comparisons — Record Quality Audit, Exact Row Deduplication & Trailing Mean Series
x402
Numeric Precision Comparisons — Record Quality Audit, Exact Row Deduplication & 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).
Runs three independent analytical reports (record quality audit, exact row deduplication, and trailing mean series) on up to 200 rows of numeric source/transformed value pairs, using local analytical engines with no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Numeric precision comparisons — Record quality audit + Exact row deduplication + Trailing mean series: Record quality audit + Exact row deduplication + Trailing mean series for supplied numeric precision comparisons records (source_value, transformed_value). 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
Three independent analytical reports: (1) a record quality audit flagging completeness and precision issues across source/transformed value pairs; (2) an exact row deduplication report identifying duplicate rows within the dataset; (3) a trailing mean series computed over a specified field (source_value or transformed_value) with the requested window size. All computed locally from supplied data, no external data fetched.
Example use cases
ETL pipeline transformation validation
“I just ran an ETL job on my pricing data — can you do a quality audit and deduplication check on these 50 rows of source vs transformed values, grouped by region, dated from 2026-09-01 through 2026-09-10? Also compute a trailing mean series with a window of 5 on the source_value field so I can spot anomalies.”
Duplicate detection in financial records
“I need to find exact duplicate rows in my financial transaction dataset — here are 80 rows with row IDs, dates from 2026-01-01 to 2026-03-31, dataset groups like 'revenue' and 'expenses', and both source and transformed amounts. Can you run the full three-report analysis with a window of 4?”
Data quality audit for sensor readings
“Can you audit the quality of my sensor readings dataset? I have 120 rows with sensor IDs as row IDs, measurement dates in 2026, group labels like 'sensor-a' and 'sensor-b', raw readings as source_value and calibrated readings as transformed_value. Run all three reports using transformed_value for the trailing mean with a window of 6.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-20-216?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of record quality auditing, exact deduplication, and trailing mean series in a single API call on a batch of up to 200 numeric source/transformed value rows. It is ideal for ETL validation workflows, data transformation QA pipelines, and time-series smoothing checks where cost efficiency matters (three reports for $0.01 USDC). Prefer this over building custom scripts when you need quick, local analytical results without any external data dependencies or professional assessments.
Side effects
- Fewer than 1 row or more than 200 rows submitted — request rejected
- Window size smaller than 2 or larger than number of rows — trailing mean computation may fail or produce empty results
- Date field not matching YYYY-MM-DD pattern — row rejected or report incomplete
- source_value or transformed_value outside ±1,000,000,000,000 range — schema validation error
- row_id missing or empty — row rejected
- Invalid field enum value (not 'source_value' or 'transformed_value') — error returned
- Payment of 0.01 USDC not provided — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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