# Save Files Analyzer — String Normalisation + Date-by-Category Pivot

> Save Files Analyzer — String Normalisation + Date-by-Category Pivot 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 two analytical reports on game save-file records: string normalisation of categorical fields and a date-by-category pivot for a chosen numeric metric (save_kb or write_ms), on up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-13-035?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/save-files-analyzer-string-normalisation-date-by-category-pivot-a52ecf9e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_8ym5O7MMIsbkC0U2uGNBv

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 save-files-analyzer-string-normalisation-date-by-category-pivot-a52ecf9e -d '<json body>'
```

Example prompt: I have a batch of up to 200 game save records with fields row_id, game_area, date, save_kb, and write_ms — can you run the string normalisation and date-by-category pivot reports on them using save_kb as the metric?

## When to prefer this

Choose this endpoint when you have game save-file records (up to 200 rows) and need both string normalisation of categorical labels and a date-by-category pivot on a numeric metric in a single low-cost call. It is ideal for lightweight, offline analytical summaries of save performance data without needing a full data warehouse or live lookup service.

## Known failure modes

- Rows exceed 200-item limit — request rejected
- Missing required fields (row_id, game_area, date, save_kb, write_ms) — validation error
- Invalid date format (must match YYYY-MM-DD) — schema rejection
- Numeric values outside allowed range — validation failure
- Empty rows array — minimum 1 item required
- Invalid field selector (must be save_kb or write_ms) — enum validation error

## How this service works

Save files — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied save files records (save_kb, write_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

Two structured reports: (1) a string-normalised view of the game_area categorical field across all supplied rows, and (2) a date-by-category pivot table showing aggregated values of the chosen numeric field (save_kb or write_ms) broken down by date and game_area. All computation is performed locally on the supplied data — no external lookups or predictions.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/save-files-analyzer-string-normalisation-date-by-category-pivot-a52ecf9e/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)