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Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Adjacent Row Changes
x402
Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Adjacent Row Changes 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).
Analyzes up to 200 duplicate-observation records and returns three independent reports: a record quality audit, string normalization analysis, and adjacent row change detection.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes 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: (1) a record quality audit identifying missing, invalid, or anomalous values in the supplied rows; (2) a string normalization report flagging inconsistencies in text fields such as dataset_group; and (3) an adjacent row change report highlighting significant shifts between consecutive records in the selected numeric field (duplicate_rows or total_rows). All analysis runs locally with no external lookups.
Example use cases
Dataset deduplication quality check
“I have a batch of 80 daily duplicate-observation records for datasets 'inventory' and 'sales', each with a date, duplicate_rows, and total_rows — can you run a quality audit and flag any string normalization issues in the dataset group names for the duplicate_rows field?”
Adjacent row anomaly detection
“My ETL pipeline logs duplicate_rows and total_rows for each dataset every day — can you analyze 120 of these rows and tell me where the duplicate_rows value changes sharply between consecutive rows, plus give me a quality audit?”
Cross-group duplicate rate review
“I need to audit duplicate observation records across three dataset groups — 'orders', 'customers', and 'products' — for the past 60 days, up to 150 rows total. Give me all three reports: quality audit, string normalization, and adjacent changes, focused on the total_rows field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, low-cost ($0.01) multi-angle quality analysis of duplicate observation records — specifically when you want all three of record quality audit, string normalization, and adjacent row change detection in a single call for up to 200 rows, without any need for live data lookup or external prediction.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- Numeric values outside allowed range (-1T to +1T) may be rejected
- Empty rows array returns error
- Payment of 0.01 USDC not provided — request rejected with 402
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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