# Simulation Caches — Date×Category Pivot + Explicit Missing-Value Fill

> Simulation Caches — Date×Category Pivot + 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).

Pivots simulation cache records (cache_gb, simulation_seconds) by date and asset group category, then fills missing values using zero, mean, or median imputation — returns two analytical reports per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-08-189?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-date-category-pivot-explicit-missing-value-fill-fe319791
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_dYg61yY-4zhlhBGJXYXGI

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-date-category-pivot-explicit-missing-value-fill-fe319791 -d '<json body>'
```

Example prompt: I have simulation cache records with columns row_id, asset_group, date, cache_gb, and simulation_seconds — can you pivot them by date and asset group, and fill any missing cache_gb values using median imputation? Here's my data: group-a had 10 GB on 2026-10-01 and 15 GB on 2026-10-03, group-b had 20 GB on 2026-10-02 and 30 GB on 2026-10-04.

## When to prefer this

Choose this endpoint when you have simulation cache tabular data (cache_gb and/or simulation_seconds) keyed by date and asset group and need both a date-by-category pivot reshaping AND explicit missing-value imputation (zero, mean, or median) in a single low-cost call. Ideal for offline/batch analytical prep of up to 200 rows where no live data lookup, prediction, or external action is needed. At $0.01 USDC you get two independent analytical reports simultaneously, making it more efficient than running pivot and imputation separately.

## Known failure modes

- Input array is empty or missing — minItems:1 violation returns validation error
- row_id field missing or blank — required field violation
- date field does not match YYYY-MM-DD pattern — pattern validation error
- More than 200 rows submitted — maxItems:200 exceeded
- cache_gb or simulation_seconds out of allowed numeric range — schema rejection
- Invalid enum value for 'field' or 'method' parameters — validation error
- All values missing for the selected field — imputation may produce degenerate output

## How this service works

Simulation caches — Date by category pivot + Explicit missing-value fill: Date by category pivot + 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 based on the supplied simulation cache records: (1) a date-by-category pivot table showing the selected metric (cache_gb or simulation_seconds) spread across asset groups on each date, and (2) the same dataset with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports are computed locally with no external lookups. Up to 200 rows supported per call.

## 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-date-category-pivot-explicit-missing-value-fill-fe319791/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)