# UI Screens Analytics — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill

> UI Screens Analytics — Secondary-Measure Weighted Mean + 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 2026-10-24).

Computes a secondary-measure weighted mean and fills missing values for up to 200 UI screen records containing widget_count and render_ms fields, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-16-187?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/ui-screens-analytics-secondary-measure-weighted-mean-explicit-missing-87025666
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ccZprv_bwCVn6Wffe8enH

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-analytics-secondary-measure-weighted-mean-explicit-missing-87025666 -d '<json body>'
```

Example prompt: I have UI screen records for two game areas with widget_count and render_ms values — some entries have nulls. Can you compute the secondary-measure weighted mean for render_ms and fill the missing values using median imputation across my 50 rows?

## When to prefer this

Choose this endpoint when you have tabular UI screen records (with widget_count and render_ms) and need both a weighted mean aggregation and explicit missing-value imputation in a single call. Ideal for game UI performance analytics pipelines where you want two bundled statistical reports at low cost ($0.01 USDC) without external lookups or predictions. Prefer this over general-purpose analytics when your data fits the specific schema (row_id, game_area, date, widget_count, render_ms) and you need offline, deterministic statistical computation.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, game_area) on any row — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- Payment not received or insufficient USDC — x402 payment required response
- All values null for chosen field — weighted mean may be undefined or return NaN
- Invalid enum value for field or method parameters — schema rejection

## How this service works

UI screens — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + 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: (1) a secondary-measure weighted mean result for the chosen field (widget_count or render_ms) using the other metric as weight, and (2) an explicit missing-value fill result applying the selected method (zero, mean, or median) across up to 200 supplied UI screen rows. No external lookups 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-analytics-secondary-measure-weighted-mean-explicit-missing-87025666/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)