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Transformation Throughput Analytics — Record Quality Audit, String Normalisation & Duplicate Key Report
x402
Transformation Throughput Analytics — Record Quality Audit, String Normalisation & Duplicate Key Report is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs three local analytical reports (record quality audit, string normalisation, and duplicate key detection) on supplied transformation throughput records containing processed_rows and duration_seconds fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transformation throughput — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report for supplied transformation throughput records (processed_rows, duration_seconds). 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 independent analytical reports: (1) a record quality audit highlighting missing, null, or out-of-range values in the supplied rows; (2) a string normalisation report identifying and correcting inconsistencies in text fields such as dataset_group; and (3) a duplicate key report flagging row_id or composite key collisions across the submitted records. All analysis is performed locally using shared analytical engines on up to 200 rows per call.
Example use cases
ETL batch quality check before reporting
“I have a week's worth of ETL run records — each with a row ID, dataset group, date, processed row count, and duration in seconds. Can you audit them for quality issues, normalise any inconsistent string fields, and flag any duplicate row IDs before I send the summary to my data team?”
Detecting duplicate keys in pipeline logs
“Our pipeline logging system may have accidentally written duplicate entries. I've got about 150 throughput records with row IDs, group names, dates, processed row counts, and durations — can you run a duplicate key report on them and also do a quality audit at the same time?”
Normalising dataset group names across transformation runs
“We've got transformation throughput records from multiple systems and the dataset_group field has inconsistent casing and whitespace. Can you normalise those strings and also flag any quality issues or duplicate keys across the 80 records I'll send you?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-13-192?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical reports — quality audit, string normalisation, and duplicate key detection — on transformation throughput records (processed_rows, duration_seconds) in a single low-cost call. It is ideal for data engineering pipelines where ETL batch metrics need to be validated offline without any external lookups or live data fetching. Prefer it over separate single-report endpoints when all three analyses are needed together for up to 200 rows at $0.01 USDC.
Side effects
- Submitting more than 200 rows returns an error — batch must be split
- Missing required fields (row_id, dataset_group, date, processed_rows, duration_seconds) cause row-level validation failures
- Malformed date strings not matching YYYY-MM-DD pattern are rejected
- Null numeric values are accepted but flagged in quality audit output
- Empty rows array (minItems:1 not met) returns a schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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