# Texture Baking Analytics — Grouped Numeric Summary + Secondary-Measure Weighted Mean

> Texture Baking Analytics — Grouped Numeric Summary + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-27).

Computes a grouped numeric summary and a secondary-measure weighted mean for texture baking records (bake_seconds, texture_pixels) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-10-096?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-27
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/texture-baking-analytics-grouped-numeric-summary-secondary-measure-15216fcd
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_KL55SpEHqXcnSnLcqKDpp

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 texture-baking-analytics-grouped-numeric-summary-secondary-measure-15216fcd -d '<json body>'
```

Example prompt: I have texture baking records for two asset groups — group-a and group-b — with bake_seconds and texture_pixels columns for about 50 rows. Can you run a grouped numeric summary and a weighted mean for bake_seconds (weighted by texture_pixels) across those groups?

## When to prefer this

Choose this endpoint when you have tabular texture baking records (up to 200 rows) and need both a grouped numeric summary and a weighted mean in a single low-cost call ($0.01 USDC). It is ideal for 3D asset pipeline analytics, render farm audits, or game development workflows where bake_seconds and texture_pixels are the key metrics. Prefer this over generic analytics tools when you want a paired two-report output without setting up a data warehouse or calling an external prediction service.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, asset_group, date, bake_seconds, texture_pixels) cause validation errors
- Date values not matching YYYY-MM-DD pattern will fail schema validation
- All-null values for the selected measure field may produce undefined weighted mean
- Malformed JSON body returns 400-level error
- Payment not provided or insufficient USDC balance causes 402 payment required error

## How this service works

Texture baking — Grouped numeric summary + Secondary-measure weighted mean: Grouped numeric summary + Secondary-measure weighted mean for supplied texture baking records (bake_seconds, texture_pixels). 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 grouped numeric summary of the selected primary field (bake_seconds or texture_pixels) broken down by asset_group, and (2) a weighted mean of the primary measure using the other measure as the weight — both computed locally without any external lookups or predictions, for up to 200 input rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/texture-baking-analytics-grouped-numeric-summary-secondary-measure-15216fcd/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)