# Save Files Analyzer — Adjacent Row Changes & Missing-Value Fill

> Save Files Analyzer — Adjacent Row Changes & 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 2026-10-30).

Analyzes save file metrics (save_kb, write_ms) by computing adjacent row changes and filling missing values using a chosen method (zero, mean, or median), returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-13-162?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-adjacent-row-changes-missing-value-fill-8b05f605
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_YTDkwqCcH-4vQTmUChjfe

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-adjacent-row-changes-missing-value-fill-8b05f605 -d '<json body>'
```

Example prompt: I have save file records for my game with save_kb and write_ms columns across several game areas and dates — can you analyze adjacent row changes and fill any missing save_kb values using median imputation? Here are my rows: row-1 on 2026-10-01 in group-a with save_kb=10 and write_ms=1, row-2 on 2026-10-02 in group-b with save_kb=null and write_ms=2, row-3 on 2026-10-03 in group-a with save_kb=15 and write_ms=3.

## When to prefer this

Choose this endpoint when you need to analyze sequential change patterns in game save file metrics (save_kb, write_ms) and simultaneously impute missing values in a single call for up to 200 rows. It is purpose-built for game telemetry datasets and provides two focused analytical reports at low cost ($0.01 USDC). Prefer it over general-purpose analytics tools when your data already fits the save-file schema (row_id, game_area, date, save_kb, write_ms) and you need both delta analysis and missing-value handling without external data dependencies.

## Known failure modes

- More than 200 rows submitted — request exceeds maxItems limit
- Missing required fields (row_id, game_area, date, save_kb, write_ms) in any row — validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- Invalid field enum value (not save_kb or write_ms) — schema rejection
- Invalid method enum value (not zero, mean, or median) — schema rejection
- Payment not attached or insufficient — x402 payment required error
- Empty rows array — minItems violation

## How this service works

Save files — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + 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 two analytical reports: (1) an adjacent row change report showing the delta between consecutive rows for the selected metric field, and (2) a missing-value fill report showing imputed values for null entries using the chosen method (zero, mean, or median). Reports cover all supplied rows up to 200, 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/save-files-analyzer-adjacent-row-changes-missing-value-fill-8b05f605/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)