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Training Compliance Data Auditor — Quality Audit, String Normalisation & Dataset Comparison
x402
Training Compliance Data Auditor — Quality Audit, String Normalisation & 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 2026-10-26).
Runs three independent analytical reports on training compliance records (completed_courses, assigned_courses): record quality audit, string normalisation, and keyed dataset comparison for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training compliance — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied training compliance records (completed_courses, assigned_courses). 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 reports: (1) a record quality audit highlighting nulls, out-of-range values, and malformed fields; (2) a string normalisation report with standardised team_group and other text fields; (3) a keyed dataset comparison report showing row-level differences between the primary and secondary datasets matched by row_id. All processing is local with no external lookups.
Example use cases
Pre-reporting compliance data validation
“Before I submit our Q3 training compliance report, can you audit these 120 records for quality issues and normalise the team_group strings? The rows have row_id, team_group, date, completed_courses, and assigned_courses fields.”
Detecting drift between two LMS exports
“I have two exports of our learning management system from different dates — can you compare them row-by-row to find which training records changed between the two snapshots? Both have up to 80 rows with row_id, team_group, date, completed_courses, and assigned_courses.”
Cleaning inconsistent team labels in compliance data
“Our compliance dataset has messy team_group names across 60 rows — can you run string normalisation on it so we get consistent labels, and also flag any quality issues you find in the completed_courses or assigned_courses values?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-operations-10-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports on training compliance tabular data in a single low-cost call: quality auditing, string normalisation, and dataset diffing by key. It is ideal when you already have the data locally and need offline analysis without live lookups, external integrations, or ML predictions. Best suited for HR/L&D teams validating LMS exports before reporting or reconciling two compliance snapshots.
Side effects
- More than 200 rows submitted — request rejected with row count error
- Missing required fields (row_id, team_group, date, completed_courses, assigned_courses) — validation error per row
- Date field not matching YYYY-MM-DD pattern — pattern validation failure
- Numeric fields outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — range error
- Empty rows array (minItems:1 not met) — schema validation error
- Payment not included or insufficient — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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