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Pipeline Durations — Record Quality Audit + String Normalisation + Keyed Dataset Comparison
x402
Pipeline Durations — 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 2026-10-18).
Runs three analytical reports (record quality audit, string normalisation, and keyed dataset comparison) on pipeline duration records containing run_seconds and processed_rows fields, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Pipeline durations — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied pipeline durations records (run_seconds, processed_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 independent analytical reports: (1) a record quality audit flagging invalid, null, or out-of-range values in run_seconds and processed_rows; (2) a string normalisation report cleaning and standardising text fields like dataset_group; and (3) a keyed dataset comparison report showing row-level differences between the primary and secondary datasets matched by row_id.
Example use cases
ETL pipeline run health check
“I have up to 150 rows of pipeline execution logs with run_seconds and processed_rows per dataset_group — can you run a record quality audit and string normalisation on them and tell me if there are any bad records?”
Before-and-after pipeline comparison
“I have two versions of my pipeline duration dataset — the original and an updated one with the same row IDs — can you do a keyed comparison between them to highlight where run_seconds or processed_rows changed?”
Data quality gate before warehouse load
“Before I load these pipeline duration records into our data warehouse, can you audit them for quality issues, normalise the string fields, and flag anything that looks off across the dataset_group and date columns?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-05-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three pipeline-duration-specific analytical reports in a single $0.01 USDC call — particularly when you want record quality auditing, string field normalisation, and row-keyed cross-dataset comparison combined. It is best suited for offline/batch analytical use cases on up to 200 rows of pipeline execution data, not for real-time monitoring or live data lookups.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, run_seconds, processed_rows) causes row-level rejection
- Malformed date strings not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside the allowed range (-1000000000000 to 1000000000000) are rejected
- Empty rows array returns an error (minItems: 1)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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