# Render Jobs Grouped Numeric Summary with IQR Outlier Flags

> Render Jobs Grouped Numeric Summary with 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 2026-10-17).

Computes grouped descriptive statistics and IQR-based outlier flags for render job records across render_seconds or sample_count fields, covering up to 200 rows in two independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-02-094?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-17
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/render-jobs-grouped-numeric-summary-with-iqr-outlier-flags-5677f63e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_8R6-la4TdAcQVTzBKv52y

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 render-jobs-grouped-numeric-summary-with-iqr-outlier-flags-5677f63e -d '<json body>'
```

Example prompt: Can you analyze these render job records and give me a grouped numeric summary plus IQR outlier flags for the render_seconds field? Here are up to 200 rows with row_id, asset_group, date, render_seconds, and sample_count — flag any jobs that look like outliers within their group.

## When to prefer this

Choose this endpoint when you have tabular render job data grouped by asset categories and need both summary statistics and IQR-based outlier detection in a single call without any external data lookup or prediction. It is ideal for offline/batch QA workflows on up to 200 rows where local, reproducible analytics are sufficient. Prefer alternatives if you need real-time monitoring, live data ingestion, prediction, or more than 200 rows.

## Known failure modes

- Input array is empty or missing required fields (row_id, asset_group, date, render_seconds, sample_count)
- More than 200 rows submitted — exceeds the maximum item limit
- Invalid date format (must match YYYY-MM-DD pattern)
- Numeric field values outside the allowed range (-1e12 to 1e12)
- Field selector not one of the allowed enum values: render_seconds or sample_count
- All values in a group are null, preventing IQR calculation

## How this service works

Render jobs — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + IQR outlier flags for supplied render jobs records (render_seconds, sample_count). 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: (1) a grouped numeric summary (e.g. mean, median, std dev, min, max, quartiles) broken down by asset_group for the selected numeric field, and (2) IQR-based outlier flags identifying which rows deviate significantly from their group's interquartile range. 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",
     "render_seconds",
     "sample_count"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "asset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "sample_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "render_seconds": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "asset_group": "group-a",
     "sample_count": 1,
     "render_seconds": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "asset_group": "group-b",
     "sample_count": 2,
     "render_seconds": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "asset_group": "group-a",
     "sample_count": 3,
     "render_seconds": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "asset_group": "group-b",
     "sample_count": 4,
     "render_seconds": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "render_seconds",
    "sample_count"
   ],
   "type": "string",
   "default": "render_seconds"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/render-jobs-grouped-numeric-summary-with-iqr-outlier-flags-5677f63e/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)