# Simulation Caches — String Normalisation + IQR Outlier Flags

> Simulation Caches — String Normalisation + IQR Outlier Flags 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).

Normalises string fields and flags IQR-based outliers in simulation cache records (cache_gb and simulation_seconds) for up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-08-032?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-string-normalisation-iqr-outlier-flags-bfdaa2cf
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_yYTHs0Wi6sOXQo32GR41c

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-string-normalisation-iqr-outlier-flags-bfdaa2cf -d '<json body>'
```

Example prompt: Can you analyse my simulation cache records for string normalisation issues and IQR outlier flags? I have 4 rows — row-1 on 2026-10-01 for group-a with cache_gb=10 and simulation_seconds=1, row-2 on 2026-10-02 for group-b with cache_gb=20 and simulation_seconds=2, row-3 on 2026-10-03 for group-a with cache_gb=15 and simulation_seconds=3, and row-4 on 2026-10-04 for group-b with cache_gb=30 and simulation_seconds=4 — run it on the cache_gb field.

## When to prefer this

Choose this endpoint when you need both string normalisation and IQR-based outlier detection on simulation cache records in a single low-cost call. Ideal for data quality pipelines dealing with simulation_seconds and cache_gb metrics across asset groups, where you want statistical anomaly detection without external model inference or live data lookups. Best suited for batch sizes up to 200 rows where local analytical computation is sufficient.

## Known failure modes

- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds maximum row limit
- Invalid date format (non-YYYY-MM-DD) — validation error
- Numeric values outside allowed range (±1,000,000,000,000) — schema violation
- Missing required fields (row_id, asset_group, date, cache_gb, simulation_seconds) — request rejected
- Invalid field enum value (not cache_gb or simulation_seconds) — schema error
- Insufficient data variance for meaningful IQR calculation on very small datasets

## How this service works

Simulation caches — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags 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 independent analytical reports per call: (1) a string normalisation report covering the asset_group and date fields, and (2) IQR outlier flags for the specified numeric field (cache_gb or simulation_seconds), identifying which rows fall outside the interquartile range bounds. No external lookups or predictions are made; all analysis is performed locally on the supplied data.

## 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-string-normalisation-iqr-outlier-flags-bfdaa2cf/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)