# Simulation Caches — Category Frequencies + Explicit Missing-Value Fill

> Simulation Caches — Category Frequencies + 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 2026-10-21).

Computes category frequency distributions and fills missing values (cache_gb or simulation_seconds) using zero, mean, or median imputation for up to 200 simulation cache records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-08-112?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-21
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/simulation-caches-category-frequencies-explicit-missing-value-fill-55f30818
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_V40W5BSMFHfTTTntVxA79

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-category-frequencies-explicit-missing-value-fill-55f30818 -d '<json body>'
```

Example prompt: I have up to 200 simulation cache records with asset_group, date, cache_gb, and simulation_seconds — can you run category frequency analysis on asset_group and fill any missing cache_gb values using median imputation on that dataset?

## When to prefer this

Choose this endpoint when you have simulation cache records (with cache_gb and simulation_seconds fields) and need both a category frequency breakdown of asset groups AND missing-value imputation in a single low-cost call. It is ideal for lightweight, offline analytical preprocessing of up to 200 rows without needing a full data pipeline or external ML service. Not suitable for live prediction, forecasting, or datasets exceeding 200 rows.

## Known failure modes

- Rows array empty or missing — returns validation error
- More than 200 rows submitted — rejected, maxItems exceeded
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Field value not in enum [cache_gb, simulation_seconds] — rejected
- Method value not in enum [zero, mean, median] — rejected
- Numeric values outside allowed range — validation error
- Payment not included or insufficient USDC — 402 Payment Required

## How this service works

Simulation caches — Category frequencies + Explicit missing-value fill: Category frequencies + 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

Returns two analytical reports: (1) a category frequency table showing how often each asset_group value appears across the submitted rows, and (2) a filled dataset where null values in the specified numeric field (cache_gb or simulation_seconds) have been replaced using the chosen imputation method (zero, mean, or median). Both reports are computed locally with no external 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-category-frequencies-explicit-missing-value-fill-55f30818/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)