# Training Compliance String Normalisation and Missing-Value Fill

> 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.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-operations-10-037?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-compliance-string-normalisation-and-missing-value-fill-feccd2b3
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_PcflmuKBACenHF2Mv1-sV

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-compliance-string-normalisation-and-missing-value-fill-feccd2b3 -d '<json body>'
```

Example prompt: Can you clean up my training compliance data — normalise the string fields and fill in any missing completed_courses values using median imputation? I have 4 rows: row-1 for group-a on 2026-10-01 with 10 completed and 1 assigned, row-2 for group-b on 2026-10-02 with null completed and 2 assigned, row-3 for group-a on 2026-10-03 with 15 completed and 3 assigned, row-4 for group-b on 2026-10-04 with 30 completed and null assigned.

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/training-compliance-string-normalisation-and-missing-value-fill-feccd2b3/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)