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Duplicate Observations Analytics — Quality Audit, Deduplication & Daily Sum Series
x402
Duplicate Observations Analytics — Quality Audit, Deduplication & Daily Sum Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Runs three local analytical reports on duplicate-observation data: a record quality audit, exact row deduplication, and a daily sum time series, processing up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Record quality audit + Exact row deduplication + Daily sum series: Record quality audit + Exact row deduplication + Daily sum 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
Returns three independent analytical reports computed locally from the supplied rows: (1) a record quality audit flagging anomalies and data issues, (2) an exact row deduplication report identifying and collapsing duplicate records, and (3) a daily sum time series aggregating the selected field (duplicate_rows or total_rows) by date. All reports are based solely on the provided data with no external lookups.
Example use cases
Data pipeline deduplication audit
“I have a batch of 50 observation records from our ETL pipeline with dataset_group 'sales-feed' and dates ranging from 2025-01-01 to 2025-02-19. Can you run the quality audit, exact row deduplication, and daily sum series on the duplicate_rows field so I can see where our pipeline is producing duplicates?”
Daily duplicate trend reporting
“I want to track how duplicate_rows counts are trending day by day across 'region-east' and 'region-west' dataset groups for Q1 2025. Can you run the daily sum series plus the quality audit on my 120 observation records using the duplicate_rows field?”
Record quality check before data ingestion
“Before I ingest these 80 observation records into our warehouse, can you run a full quality audit and exact deduplication check on the total_rows field to flag any bad or repeated rows across the 'inventory-sync' dataset group?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-215?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three analytical perspectives — quality audit, deduplication, and daily aggregation — on duplicate observation data in a single low-cost call. It is ideal for data engineers validating ETL outputs, analysts checking time-series consistency, or automated pipelines that need a quick, offline batch report on up to 200 rows without requiring external lookups or live data connections. At $0.01 USDC for three reports, it is cost-effective compared to calling three separate services.
Side effects
- Exceeding 200 rows in the input array returns a validation error
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) cause schema validation failure
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation error
- Numeric values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) are rejected
- Supplying zero rows returns a minItems validation error
- Invalid field enum value (anything other than 'duplicate_rows' or 'total_rows') causes an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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