# Texture Streaming Data Analyzer — Quality Audit, String Normalization & Grouped Summary

> Texture Streaming Data Analyzer — Quality Audit, String Normalization & Grouped Summary is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).

Runs three local analytical reports on texture streaming records: a record quality audit, string normalization of game_area labels, and a grouped numeric summary of loaded_mb or stream_ms metrics — for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-09-194?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/texture-streaming-data-analyzer-quality-audit-string-normalization-4e771fc7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Afw-D5XoaumPv_mMXJI7e

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-streaming-data-analyzer-quality-audit-string-normalization-4e771fc7 -d '<json body>'
```

Example prompt: Can you run a quality audit, normalize the game_area strings, and give me a grouped numeric summary of loaded_mb for these texture streaming records? I have 4 rows: row-1 on 2026-10-01 in group-a with loaded_mb=10 and stream_ms=1, row-2 on 2026-10-02 in group-b with loaded_mb=20 and stream_ms=2, row-3 on 2026-10-03 in group-a with loaded_mb=15 and stream_ms=3, and row-4 on 2026-10-04 in group-b with loaded_mb=30 and stream_ms=4.

## When to prefer this

Choose this endpoint when you have up to 200 texture streaming records (with loaded_mb and stream_ms measurements) and need all three of: a record quality audit, string normalization of category labels, and a grouped numeric summary — bundled in one $0.01 call. Prefer this over single-report endpoints when you need all three analyses simultaneously without making separate API calls. Not suitable for live data lookup, prediction, or datasets exceeding 200 rows.

## Known failure modes

- More than 200 rows submitted — request rejected with row count limit error
- Missing required fields (row_id, date, game_area) in one or more rows — validation error returned
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- Field selector not one of 'loaded_mb' or 'stream_ms' — enum validation error
- Empty rows array (minItems: 1 violated) — request rejected

## How this service works

Texture streaming — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary for supplied texture streaming records (loaded_mb, stream_ms). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Returns three reports: (1) a record quality audit flagging missing, null, or malformed values per row; (2) normalized/standardized game_area string labels; and (3) a grouped numeric summary (count, sum, mean, min, max, etc.) of the selected field (loaded_mb or stream_ms) aggregated by game_area — all computed locally with no external calls.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "game_area",
     "date",
     "loaded_mb",
     "stream_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
     },
     "loaded_mb": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "stream_ms": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "game_area": "group-a",
     "loaded_mb": 10,
     "stream_ms": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "game_area": "group-b",
     "loaded_mb": 20,
     "stream_ms": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "game_area": "group-a",
     "loaded_mb": 15,
     "stream_ms": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "game_area": "group-b",
     "loaded_mb": 30,
     "stream_ms": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "loaded_mb",
    "stream_ms"
   ],
   "type": "string",
   "default": "loaded_mb"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/texture-streaming-data-analyzer-quality-audit-string-normalization-4e771fc7/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)