# Frame Timing Analyzer — Record Quality Audit, String Normalisation & Category Frequencies

> Frame Timing Analyzer — Record Quality Audit, String Normalisation & Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).

Runs three local analytical reports on frame timing data (frame_ms, draw_calls): a record quality audit, string normalisation, and category frequency counts — up to 200 rows per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-01-195?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/frame-timing-analyzer-record-quality-audit-string-normalisation-b28e0543
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_30EpS2Hakb_45o5CNtF0K

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 frame-timing-analyzer-record-quality-audit-string-normalisation-b28e0543 -d '<json body>'
```

Example prompt: Can you audit the quality of these 50 frame timing records from our game — check for missing values, normalise the game_area strings, and give me category frequencies for the game areas? Here are the rows with row_id, date, game_area, frame_ms, and draw_calls.

## When to prefer this

Choose this endpoint when you need three bundled data-quality and summarisation operations (quality audit + string normalisation + category frequencies) on game frame timing data (frame_ms and draw_calls) in a single low-cost call. It is best suited for offline or batch quality checks on datasets up to 200 rows where no live lookup, prediction, or external action is needed. Prefer it over general analytics services when you specifically need all three report types together for game performance telemetry.

## Known failure modes

- Exceeds 200-row limit — request rejected
- Missing required fields (row_id, game_area, date, frame_ms, draw_calls) in any row
- date field not matching YYYY-MM-DD pattern — validation error
- row_id empty or exceeds 80 characters — schema rejection
- Numeric values outside allowed range (±1e12) — schema rejection
- Empty rows array — minimum 1 item not met

## How this service works

Frame timing — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied frame timing records (frame_ms, draw_calls). 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 analytical reports: (1) a record quality audit flagging missing, null, or malformed values across rows; (2) normalised versions of string fields such as game_area; and (3) category frequency counts showing how often each distinct category value appears — all computed locally against the supplied data, no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/frame-timing-analyzer-record-quality-audit-string-normalisation-b28e0543/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)