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Training Assessments — String Normalisation + IQR Outlier Flags
x402
Training Assessments — String Normalisation + IQR Outlier Flags 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 flags IQR-based outliers in pre_score and post_score columns for up to 200 training assessment rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training assessments — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied training assessments records (pre_score, post_score). 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 independent analytical reports per call: one covering string normalisation of fields like course_group and date, and one flagging per-row IQR-based outliers for the selected score field (pre_score or post_score). Each report covers up to 200 input rows and is generated entirely from the supplied data with no external lookups.
Example use cases
Quality check on corporate training scores
“I have 120 training assessment records from our corporate program with row IDs, course groups, dates, and pre- and post-scores — can you normalize the string fields and flag any IQR outliers in the post_score column so I can spot unusual results before reporting?”
Detecting anomalous pre-scores in a course cohort
“Here are 80 rows of learner assessment data with row_id, course_group, date, pre_score, and post_score — please run the string normalisation and IQR outlier flagging on pre_score so I can identify which students had unusual baseline results.”
Data cleaning before training effectiveness analysis
“Before I analyse training effectiveness, I need to clean my assessment dataset — can you normalise the string fields and flag IQR outliers for the post_score across these 150 training records grouped by course_group?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-11-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, local, privacy-preserving statistical pass over training assessment data — specifically string normalisation of categorical/text fields combined with IQR-based outlier detection on numeric pre/post scores. It is ideal for pre-processing education datasets before downstream analysis, when you want two bundled reports in a single low-cost call and have no need for predictions, external lookups, or professional assessments.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, course_group, date, pre_score, post_score) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric score values outside allowed range cause schema rejection
- Empty rows array (minItems:1 violated) returns an error
- All scores identical — IQR is zero, outlier flagging may return no flags or undefined behaviour
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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