# Training Assessments — String Normalisation + IQR Outlier Flags

> 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.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-11-032?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-26
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/training-assessments-string-normalisation-iqr-outlier-flags-28b218c6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_GQEJewxCbR_Wfhk8thr50

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability training-assessments-string-normalisation-iqr-outlier-flags-28b218c6 -d '<json body>'
```

Example prompt: Can you normalize the string fields and flag any IQR outliers in these training assessment records? I have up to 50 rows with row IDs, course groups, dates, and pre_score values — please run the outlier check on pre_score.

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "course_group",
     "date",
     "pre_score",
     "post_score"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "pre_score": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "post_score": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "course_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "pre_score": 10,
     "post_score": 1,
     "course_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "pre_score": 20,
     "post_score": 2,
     "course_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "pre_score": 15,
     "post_score": 3,
     "course_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "pre_score": 30,
     "post_score": 4,
     "course_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "pre_score",
    "post_score"
   ],
   "type": "string",
   "default": "pre_score"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/training-assessments-string-normalisation-iqr-outlier-flags-28b218c6/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)