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Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Keyed Dataset Comparison
x402
Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Keyed Dataset Comparison 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 independent analytical reports (record quality audit, string normalisation, and keyed dataset comparison) on up to 200 duplicate-observation records containing duplicate_rows and total_rows fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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 flagging issues such as null values, out-of-range counts, or suspicious duplicate_rows/total_rows ratios; (2) a string normalisation report covering dataset_group and other string fields; and (3) a keyed dataset comparison report matching records from the 'rows' and 'other' input arrays by row_id to surface discrepancies in duplicate_rows and total_rows values. All analysis is performed locally with no external lookups.
Example use cases
Pipeline deduplication output validation
“I've got two snapshots of my ETL pipeline's duplicate detection results — one from before tuning and one after — each with up to 50 rows tracking duplicate_rows and total_rows per dataset_group and date. Can you run a quality audit, string normalisation, and keyed comparison between the two so I can see what changed?”
Cross-team dataset duplicate discrepancy check
“My team and the data engineering team produced separate duplicate-observation exports for the same dataset groups and dates. Can you compare the two sets — each up to 100 rows with row_id, dataset_group, date, duplicate_rows, and total_rows — and give me a record quality audit and keyed comparison report showing where the counts differ?”
Monthly duplicate rate quality review
“I need a record quality audit and string normalisation report on my monthly duplicate tracking data — about 80 rows covering different dataset_groups through October 2026, each with duplicate_rows and total_rows counts. Can you flag any quality issues or malformed string fields?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled set of three analytical reports — quality audit, string normalisation, and keyed comparison — specifically for duplicate-observation records with duplicate_rows and total_rows fields, at low cost ($0.01 USDC for all three reports on up to 200 rows). It is ideal when you want local, offline analysis with no external data lookups, and when you have two parallel datasets to compare by key. Prefer alternatives if you need real-time deduplication, live database queries, or analysis of datasets larger than 200 rows.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (-1e12 to 1e12) — schema rejection
- Empty rows array (minItems:1 not satisfied) — bad request
- Mismatched row_id keys between rows and other arrays — comparison report may show unmatched records
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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