# Simulation Caches — Numeric Row Ordering + Explicit Missing-Value Fill

> Simulation Caches — Numeric Row Ordering + 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 2026-10-22).

Sorts simulation cache records numerically by row and fills missing cache_gb or simulation_seconds values using zero, mean, or median imputation, delivering two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-08-124?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/simulation-caches-numeric-row-ordering-explicit-missing-value-fill-f1631524
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_BfIWoD5YwZ_pO4gpq4lOw

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 simulation-caches-numeric-row-ordering-explicit-missing-value-fill-f1631524 -d '<json body>'
```

Example prompt: I have simulation cache records with some missing cache_gb values — can you sort them numerically by row and fill those gaps using median imputation? Here are up to 200 rows with fields row_id, asset_group, date, cache_gb, and simulation_seconds.

## When to prefer this

Choose this endpoint when you need to simultaneously sort simulation cache records by numeric row order AND impute missing numeric values in cache_gb or simulation_seconds in a single low-cost call. It is ideal for preprocessing simulation performance datasets before aggregation or reporting, especially when null handling strategy (zero, mean, or median) needs to be explicit. Prefer it over generic data tools when your data fits the simulation cache schema (asset_group, date, cache_gb, simulation_seconds) and you want two bundled analytical outputs for $0.01 USDC.

## Known failure modes

- Rows array is empty or missing — validation error returned
- More than 200 rows submitted — request rejected
- Invalid date format (not YYYY-MM-DD) on a row — schema validation failure
- Unrecognized fill method or field enum value — bad request error
- All values in selected field are null — mean/median fill may be undefined or return zero
- row_id missing or empty string — schema validation failure

## How this service works

Simulation caches — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill for supplied simulation caches records (cache_gb, simulation_seconds). 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 analytical reports covering the submitted simulation cache records: one showing rows sorted in numeric order and one showing the dataset with missing values in the selected field (cache_gb or simulation_seconds) explicitly filled using the chosen method (zero, mean, or median). Up to 200 rows are processed per call using local analytical engines with no external data lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/simulation-caches-numeric-row-ordering-explicit-missing-value-fill-f1631524/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)