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Duplicate Observations Record Quality Audit and String Normalisation
x402
Duplicate Observations Record Quality Audit and String Normalisation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Audits and normalises up to 200 rows of duplicate-observation records, producing a record quality report and a string normalisation report using local analytical engines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied duplicate observations records (duplicate_rows, total_rows). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent reports: (1) a record quality audit assessing the integrity, completeness, and plausibility of the supplied duplicate_rows and total_rows figures across all input rows; (2) a string normalisation report identifying and correcting inconsistencies in text fields such as dataset_group and date. All analysis is performed locally with no external lookups or live data access.
Example use cases
Monthly deduplication pipeline audit
“I have a batch of 50 rows from our monthly ETL run showing duplicate_rows and total_rows per dataset_group and date — can you run a record quality audit and string normalisation check on all of them so I can see where our deduplication is failing?”
Dataset group label normalisation check
“Our dataset_group labels have inconsistent capitalisation and spacing across these 80 duplicate observation records — can you run a string normalisation report on them alongside a full record quality audit?”
Data quality gate before warehouse load
“Before we load these 120 rows of duplicate observation counts into the warehouse, can you do a record quality audit and string normalisation pass on the duplicate_rows and total_rows fields to catch any anomalies or formatting issues?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-001?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of pre-collected duplicate observation records (duplicate_rows, total_rows, dataset_group, date) and need both a structured record quality audit and a string normalisation report in a single low-cost call. It is ideal for automated data pipeline checkpoints, pre-load validation steps, or periodic data quality reviews where no live external lookups, predictions, or professional assessments are required. Prefer alternatives if you need null coverage analysis, trend analysis, or correlation reports — those are covered by sibling endpoints on the same platform.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- duplicate_rows or total_rows outside numeric bounds — rejection
- Empty rows array (minItems:1 not met) — error returned
- Payment of 0.01 USDC not completed — 402 response blocking execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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