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Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Trailing Mean Series
x402
Duplicate Observations Analytics — Record Quality Audit, String Normalisation & 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 2026-10-16).
Runs three local analytical reports (record quality audit, string normalisation, and trailing mean series) on up to 200 rows of duplicate-observation data containing duplicate_rows and total_rows counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied duplicate observations records (duplicate_rows, total_rows). 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 issues in the supplied rows, (2) a string normalisation report identifying and correcting inconsistencies in string fields like dataset_group, and (3) a trailing mean series computed over the chosen numeric field (duplicate_rows or total_rows) using the specified window size. All reports are computed locally from the supplied data with no external lookups.
Example use cases
Data pipeline duplicate rate audit
“I've got a batch of 50 date-stamped records across four dataset groups, each with a duplicate_rows and total_rows count. Can you run the record quality audit and string normalisation reports on them using a trailing mean window of 3 over the duplicate_rows field?”
Trailing mean trend analysis for deduplication
“Here are 100 rows of duplicate observation data spanning three months for groups like 'sales' and 'marketing' — can you compute a trailing mean series with a window of 5 over the total_rows field and include the quality audit?”
Dataset group string consistency check
“I suspect the dataset_group labels in my 30 duplicate-observation records are inconsistently capitalised or spaced. Can you run the string normalisation report alongside the record quality audit so I can see what needs cleaning?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical reports on duplicate-observation data in a single low-cost call — specifically record quality auditing, string normalisation of group labels, and a trailing moving average — without requiring any external lookups, predictions, or live data. It is best suited for data engineers auditing ETL pipeline output, analysts reviewing deduplication metrics over time, or automated agents performing scheduled data quality checks on batch record sets of up to 200 rows.
Side effects
- More than 200 rows submitted — exceeds maxItems limit and will be rejected
- Invalid date format not matching YYYY-MM-DD pattern
- Window value outside the 2–200 integer range
- Numeric values for duplicate_rows or total_rows outside the allowed ±1,000,000,000,000 range
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) on any row
- Invalid field enum — must be exactly 'duplicate_rows' or 'total_rows'
- Payment failure via x402 protocol preventing execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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