# Simulation Caches IQR Outlier Flags + Secondary-Measure Weighted Mean

> Simulation Caches IQR Outlier Flags + Secondary-Measure Weighted Mean 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).

Analyzes simulation cache records to flag IQR-based outliers and compute a secondary-measure weighted mean, returning two statistical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-08-177?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-iqr-outlier-flags-secondary-measure-weighted-mean-e75ecf3d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ZFhiAUNDfGnyNxp9XnP9d

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-iqr-outlier-flags-secondary-measure-weighted-mean-e75ecf3d -d '<json body>'
```

Example prompt: Flag IQR outliers and compute a simulation_seconds-weighted mean for my simulation cache records — I have 4 rows: row-1 on 2026-10-01 with cache_gb=10 and simulation_seconds=1 for group-a, row-2 on 2026-10-02 with cache_gb=20 and simulation_seconds=2 for group-b, row-3 on 2026-10-03 with cache_gb=15 and simulation_seconds=3 for group-a, row-4 on 2026-10-04 with cache_gb=30 and simulation_seconds=4 for group-b; analyze on the cache_gb field.

## When to prefer this

Choose this endpoint when you need fast, local statistical analysis of simulation cache records without external data dependencies — specifically when you want both IQR-based outlier detection and a weighted mean computed together in a single call for up to 200 rows. It is ideal for data quality checks, anomaly detection in simulation infrastructure, and performance benchmarking across asset groups. Prefer it over general-purpose analytics when your data fits the cache_gb/simulation_seconds schema and you want two bundled reports at low cost.

## Known failure modes

- More than 200 rows submitted — request rejected due to row limit
- Missing required fields (row_id, date, cache_gb, simulation_seconds, asset_group) — validation error
- Date not in YYYY-MM-DD format — schema validation failure
- Null values in the selected analysis field may affect IQR computation
- Invalid field enum value (must be 'cache_gb' or 'simulation_seconds') — schema rejection
- Empty rows array — minimum 1 row required

## How this service works

Simulation caches — IQR outlier flags + Secondary-measure weighted mean: IQR outlier flags + Secondary-measure weighted mean 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 statistical reports: (1) IQR outlier flags for each supplied row indicating whether its primary field value (cache_gb or simulation_seconds) is an outlier based on interquartile range, and (2) a weighted mean of the secondary measure across the supplied records. All computation is local 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/simulation-caches-iqr-outlier-flags-secondary-measure-weighted-mean-e75ecf3d/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)