# Curriculum Mapping String Normalisation and Missing-Value Fill

> Curriculum Mapping 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 2026-10-24).

Normalises strings and fills missing values in curriculum mapping records (mapped_outcomes, required_outcomes) for up to 200 rows, producing 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-24-037?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/curriculum-mapping-string-normalisation-and-missing-value-fill-0b70ac2a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_W18zxkk9ay9TwEfD0-l5-

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 curriculum-mapping-string-normalisation-and-missing-value-fill-0b70ac2a -d '<json body>'
```

Example prompt: Clean my curriculum mapping dataset — I have up to 50 rows with row_id, course_group, date, mapped_outcomes, and required_outcomes fields; fill any missing mapped_outcomes values using the median method and give me both the string normalisation and missing-value fill reports.

## When to prefer this

Choose this endpoint when you need lightweight, deterministic string normalisation and missing-value imputation on curriculum mapping records (mapped_outcomes, required_outcomes) without any live data lookup, prediction, or external action. It is ideal for pre-processing educational outcome data before reporting or analysis, particularly when you need two independent reports (normalisation + fill) in a single cheap call ($0.01 USDC) for batches of up to 200 rows.

## Known failure modes

- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, date, mapped_outcomes, required_outcomes) in any row — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Invalid enum value for field or method parameter — bad request error
- Empty rows array — minimum 1 item required

## How this service works

Curriculum mapping — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied curriculum mapping records (mapped_outcomes, required_outcomes). 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

Returns two analytical reports: (1) a string normalisation report covering all supplied curriculum mapping records, and (2) an explicit missing-value fill report for the specified field (mapped_outcomes or required_outcomes) using the chosen fill method (zero, mean, or median). No external lookups or predictions are made; all analysis uses local engines on the supplied data.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/curriculum-mapping-string-normalisation-and-missing-value-fill-0b70ac2a/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)