# UI Screens — String Normalisation + Explicit Missing-Value Fill

> UI Screens — String Normalisation + 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).

Normalises string fields and fills missing numeric values (widget_count or render_ms) in UI screen records using zero, mean, or median imputation, producing two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-16-037?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/ui-screens-string-normalisation-explicit-missing-value-fill-9b0c230a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_r2ht1p5VAb0YPf4jJiUSy

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 ui-screens-string-normalisation-explicit-missing-value-fill-9b0c230a -d '<json body>'
```

Example prompt: I have up to 200 rows of UI screen records with widget_count and render_ms fields — some render_ms values are missing. Can you run string normalisation and fill in the missing render_ms values using median imputation, and give me two analytical reports?

## When to prefer this

Choose this endpoint when you need lightweight, local data cleaning on UI screen datasets of up to 200 rows — specifically combining string normalisation with explicit missing-value fill for widget_count or render_ms in a single call. Prefer it over general-purpose ETL tools when you want two ready-made analytical reports at low cost ($0.01 USDC) without sending data to external prediction or ML services.

## Known failure modes

- Rows array empty or exceeds 200 items — validation error returned
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Unknown imputation method value outside zero/mean/median enum — rejected
- Numeric field values outside allowed range — validation error
- Missing required fields (row_id, game_area, date) in any row — error response
- Payment of 0.01 USDC not provided or rejected — 402 payment required

## How this service works

UI screens — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied ui screens records (widget_count, render_ms). 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 independent analytical reports based on the supplied UI screen records: one covering string normalisation results and one covering explicit missing-value fill for the chosen numeric field (widget_count or render_ms) using the selected method (zero, mean, or median). No external lookups or predictions are performed; all computation uses local analytical engines.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/ui-screens-string-normalisation-explicit-missing-value-fill-9b0c230a/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)