# Physics Scenes — Record Quality Audit + String Normalisation + Trailing Mean Series

> Physics Scenes — Record Quality Audit + String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-03).

Runs three local analytical reports on physics scene records (body_count, step_ms): a data quality audit, string normalisation, and a trailing mean time series — all offline, no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-08-199?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-03
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/physics-scenes-record-quality-audit-string-normalisation-trailing-728f8811
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_lcwZl2woYt1zdal3j9IZj

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 physics-scenes-record-quality-audit-string-normalisation-trailing-728f8811 -d '<json body>'
```

Example prompt: Can you audit the quality of my physics scene records, normalise the string fields, and compute a trailing mean series with a window of 3 for body_count? I have 4 rows: row-1 on 2026-10-01 in group-a with body_count 10 and step_ms 1, row-2 on 2026-10-02 in group-b with body_count 20 and step_ms 2, row-3 on 2026-10-03 in group-a with body_count 15 and step_ms 3, row-4 on 2026-10-04 in group-b with body_count 30 and step_ms 4.

## When to prefer this

Choose this endpoint when you need three bundled local analytics reports — quality audit, string normalisation, and trailing mean series — specifically for physics scene datasets containing body_count and step_ms fields, at low cost ($0.01 for all three). Ideal when you want offline computation with no external lookups, predictions, or API calls, and your dataset fits within 200 rows. Prefer alternatives if you need live data enrichment, prediction, or reports on different domain schemas (e.g. financial, municipal, or project data).

## Known failure modes

- Fewer than 1 row submitted returns a validation error
- More than 200 rows submitted exceeds the row limit and is rejected
- Invalid date format (not YYYY-MM-DD) causes a schema validation error
- Window size larger than the number of submitted rows may produce empty or truncated trailing mean results
- Numeric fields outside the allowed range (-1e12 to 1e12) are rejected
- Missing required fields (row_id, game_area, date, body_count, step_ms) cause schema validation failure
- Non-integer window value causes a type validation error

## How this service works

Physics scenes — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied physics scenes records (body_count, step_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

Three independent analytical reports: (1) a record quality audit flagging missing values, out-of-range entries, and structural issues in the supplied rows; (2) a string normalisation report for categorical string fields like game_area; (3) a trailing mean series for the selected numeric field (body_count or step_ms) over the specified rolling window. All computed locally from submitted data — no external data sources involved. Up to 200 rows per call.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/physics-scenes-record-quality-audit-string-normalisation-trailing-728f8811/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)