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Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Daily Sum Series
x402
Duplicate Observations Analytics — Record Quality Audit, String Normalisation & 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 (record quality audit, string normalisation, and daily sum series) on up to 200 rows of duplicate-observation data containing duplicate_rows and total_rows fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + 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
Three distinct analytical reports returned together: (1) a record quality audit summarising anomalies or issues in the submitted rows, (2) a string normalisation report highlighting inconsistencies in text fields like dataset_group, and (3) a daily sum series aggregating the selected numeric field (duplicate_rows or total_rows) by date — all computed locally without external lookups.
Example use cases
Data pipeline duplicate rate audit
“I have a batch of 50 rows from our ingestion pipeline, each with a row_id, dataset_group, date, duplicate_rows count, and total_rows count. Can you run the record quality audit, string normalisation check, and daily sum series on them using the duplicate_rows field so I can see where data quality is breaking down?”
Daily duplicate trend summary
“Here are 120 daily observation records from our analytics warehouse — each has a date, dataset_group, duplicate_rows, and total_rows. Please generate the daily sum series for total_rows so I can spot which days had the highest record volumes.”
Dataset group string cleanup check
“I've got 80 rows of duplicate-detection logs with mixed-case and inconsistent dataset_group labels. Can you run the string normalisation report alongside the record quality audit on these rows so I can see which group names need cleaning up?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-198?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, daily sum series) on a compact dataset of duplicate-observation records (up to 200 rows) in a single low-cost call, without requiring live lookups or external data enrichment. It is ideal for offline data quality checks on pipeline outputs or warehouse exports where the data is already in hand.
Side effects
- Fewer than 1 or more than 200 rows submitted — rejected with validation error
- Invalid date format (not YYYY-MM-DD) — row fails schema validation
- Null or out-of-range numeric values for duplicate_rows or total_rows — may cause partial computation
- Unrecognised field selector value (not 'duplicate_rows' or 'total_rows') — rejected by enum validation
- Missing required fields (row_id, dataset_group, date) — row rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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