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Duplicate Observations Analytics — Record Quality Audit + String Normalisation + Category Frequencies
x402
Duplicate Observations Analytics — Record Quality Audit + String Normalisation + Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs three local analytical reports (record quality audit, string normalisation, and category frequencies) on up to 200 rows of duplicate-observation data (duplicate_rows, total_rows) without any external lookups or predictions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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 structured analytical reports returned together: (1) a record quality audit flagging missing, null, or out-of-range values across the supplied rows; (2) a string normalisation report identifying inconsistencies and suggested normalised forms for text fields like dataset_group; and (3) a category frequency table showing the distribution and counts of each unique dataset_group value across the supplied rows.
Example use cases
Deduplication pipeline health check
“I have a batch of 50 dataset records from our ETL pipeline — each has a dataset_group, a date, and counts of duplicate_rows vs total_rows. Can you run the record quality audit, string normalisation check, and category frequency breakdown on all of them so I can see where data quality is weakest?”
Dataset group label consistency audit
“Our data team uses various spellings and casings for dataset group names like 'Group-A', 'group_a', 'GROUPA' across about 80 rows of duplicate tracking logs from Q3. Can you run the string normalisation and category frequency reports to flag inconsistencies and show me how often each group appears?”
Weekly duplicate observation monitoring
“Every Monday I want to review last week's duplicate observation stats — I'll give you 200 rows with row IDs, dates, dataset groups, and duplicate vs total row counts. Please produce the record quality audit and category frequency report so I can spot any groups with unusually high duplicate rates.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, category frequencies) on duplicate-observation records in a single low-cost call. It is ideal for batch profiling up to 200 rows of deduplification tracking data without any external data lookups, predictions, or live integrations. Prefer it over single-report alternatives when you need all three report types simultaneously on the same dataset to save on per-call costs.
Side effects
- Fewer than 1 row supplied — request rejected (minItems: 1)
- More than 200 rows supplied — request rejected (maxItems: 200)
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) — schema validation error
- row_id exceeds 80 characters — schema validation error
- Payment of 0.01 USDC not fulfilled — 402 Payment Required response
- Numeric values outside allowed range (±1,000,000,000,000) — schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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