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Training Compliance String Normalisation and Missing-Value Fill
x402
Training Compliance String Normalisation and Missing-Value Fill 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).
Normalises string fields and fills missing values in training compliance records (completed_courses, assigned_courses) for up to 200 rows, producing 2 analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training compliance — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied training compliance records (completed_courses, assigned_courses). 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
Two analytical reports: one covering string normalisation of team_group and date fields, and one covering explicit missing-value fill for the specified numeric field (completed_courses or assigned_courses) using the chosen method (zero, mean, or median). Results are derived entirely from the supplied rows with no external lookups.
Example use cases
Fill missing completed_courses for HR report
“I have a training compliance export with some null completed_courses values across my team groups — can you fill the gaps using median imputation and normalise the string fields? Here are my 5 rows: row-1 group-alpha 2026-09-01 completed=null assigned=5, row-2 group-beta 2026-09-02 completed=8 assigned=10, row-3 group-alpha 2026-09-03 completed=6 assigned=5, row-4 group-beta 2026-09-04 completed=null assigned=10, row-5 group-gamma 2026-09-05 completed=12 assigned=8.”
Standardise compliance records before dashboard load
“Before I push our training compliance data into the dashboard, can you normalise the string fields and fill any missing assigned_courses values using zero fill? I've got 3 rows: row-A team-north 2026-11-01 completed=20 assigned=null, row-B team-south 2026-11-02 completed=15 assigned=25, row-C team-north 2026-11-03 completed=null assigned=30.”
Mean imputation for L&D compliance audit
“Our L&D audit needs clean data — please normalise strings and fill missing completed_courses using mean imputation across these 6 compliance records: row-1 dept-eng 2026-08-01 completed=50 assigned=60, row-2 dept-eng 2026-08-02 completed=null assigned=60, row-3 dept-sales 2026-08-03 completed=40 assigned=50, row-4 dept-sales 2026-08-04 completed=45 assigned=null, row-5 dept-ops 2026-08-05 completed=55 assigned=70, row-6 dept-ops 2026-08-06 completed=null assigned=70.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-operations-10-037?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, local data cleaning on training compliance records — specifically string normalisation and missing-value imputation for completed_courses or assigned_courses — without any external API calls, predictions, or professional assessments. Ideal for preprocessing compliance data before loading into dashboards or reports, when the dataset is under 200 rows and you need two bundled cleaning reports for a flat $0.01 fee.
Side effects
- More than 200 rows submitted — request rejected with validation error
- Invalid date format (not YYYY-MM-DD) — row rejected or error returned
- Unknown fill method value outside zero/mean/median enum — validation failure
- Empty rows array — minimum 1 row required
- Numeric values outside allowed range (-1e12 to 1e12) — validation error
- Missing required fields (row_id, team_group, date) — schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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