# Save Files Analytics — Base-100 Series + Explicit Missing-Value Fill

> Save Files Analytics — Base-100 Series + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-06).

Computes a base-100 indexed time series and fills missing values (zero, mean, or median) for save file metrics (save size in KB and write duration in ms) across up to 200 rows, returning 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-13-169?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-06
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/save-files-analytics-base-100-series-explicit-missing-value-fill-80452263
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_gqI0J-AZwZCfze_QIw3L7

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-analytics-base-100-series-explicit-missing-value-fill-80452263 -d '<json body>'
```

Example prompt: I have game save file records for two areas (group-a and group-b) from October 2026 — up to 50 rows with some null save_kb values. Can you normalize the save_kb field to a base-100 index starting from row 0 and fill any missing values using median imputation?

## When to prefer this

Choose this endpoint when you have tabular game save file data (save size or write duration) with potential missing values and need both a base-100 normalized index series and an imputed series in a single call. Ideal for game analytics pipelines comparing save performance across areas over time. Not suitable for live data fetching, prediction, or non-game-file datasets.

## Known failure modes

- Fewer than 1 row provided — schema requires minItems: 1
- More than 200 rows submitted — exceeds maxItems: 200 limit
- Invalid date format — must match YYYY-MM-DD pattern
- base_index out of range — must be between 0 and 199 and within actual row count
- Invalid field value — must be 'save_kb' or 'write_ms'
- Invalid method value — must be 'zero', 'mean', or 'median'
- All values for selected field are null — mean/median imputation undefined

## How this service works

Save files — Base-100 series + Explicit missing-value fill: Base-100 series + Explicit missing-value fill 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

Returns 2 analytical reports: (1) a base-100 indexed time series for the selected field (save_kb or write_ms) anchored at the specified base_index row, and (2) the same series with missing values explicitly filled using the chosen method (zero, mean, or median). Results are computed locally with no external lookups.

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/save-files-analytics-base-100-series-explicit-missing-value-fill-80452263/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)