# Training Assessments Base-100 Series + Explicit Missing-Value Fill

> Training Assessments Base-100 Series + Explicit 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).

Analyzes training assessment pre/post scores by generating a base-100 indexed series and filling missing values using a chosen imputation method, producing two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-11-169?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-base-100-series-explicit-missing-value-fill-3406b96e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_I0xjH6YUd4t2wbljUga4u

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-base-100-series-explicit-missing-value-fill-3406b96e -d '<json body>'
```

Example prompt: I have training assessment records for two course groups with up to 50 rows of pre_score and post_score data — some values are missing. Can you index the post_score field to a base-100 series starting at row 0, fill missing values using the median method, and give me the two analytical reports?

## When to prefer this

Choose this endpoint when you need to both normalize training assessment scores against a baseline index AND handle missing pre_score/post_score values in a single call, for datasets up to 200 rows. It is ideal for L&D analytics pipelines where raw assessment exports contain nulls and need standardized scoring before reporting. Prefer this over general statistics endpoints when your data is specifically structured as course-group training records with pre/post measurements.

## Known failure modes

- Exceeding 200 rows returns an error or truncated result
- Invalid date format (not YYYY-MM-DD) causes validation failure
- Missing required fields (row_id, course_group, date) cause row rejection
- base_index out of range (0-199) returns an error
- Scores outside the numeric range trigger validation errors
- Empty rows array returns an error

## How this service works

Training assessments — Base-100 series + Explicit missing-value fill: Base-100 series + Explicit missing-value fill 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

Returns two analytical reports: (1) a base-100 indexed series of the chosen score field relative to the specified base row, and (2) the dataset with missing values explicitly filled using the chosen imputation method (zero, mean, or median). Both reports are computed locally 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",
     "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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/training-assessments-base-100-series-explicit-missing-value-fill-3406b96e/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)