# Render Jobs String Normalisation and Two-Measure Correlation

> Render Jobs String Normalisation and Two-Measure Correlation 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).

Performs string normalisation and two-measure Pearson/rank correlation analysis on supplied render job records (render_seconds, sample_count) for up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-02-033?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/render-jobs-string-normalisation-and-two-measure-correlation-f44e6acf
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Hnj6JXHL39ZMaGc2kjHcD

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-string-normalisation-and-two-measure-correlation-f44e6acf -d '<json body>'
```

Example prompt: Can you normalize the strings and run a two-measure correlation between render_seconds and sample_count on my render jobs data? I have rows for asset groups like 'group-a' and 'group-b' across dates in October 2026 — here are up to 200 rows with row_id, asset_group, date, render_seconds, and sample_count.

## When to prefer this

Choose this endpoint when you have render job tabular data with render_seconds and sample_count fields and need both string normalisation of text fields and a statistical correlation between the two numeric measures in a single call. It is self-contained (no live lookups), cheap at $0.01 USDC for two reports, and handles up to 200 rows. Prefer this over general-purpose statistical tools when your data specifically matches the render jobs schema (row_id, asset_group, date, render_seconds, sample_count).

## Known failure modes

- Fewer than 1 row submitted — rejected by minItems:1 constraint
- More than 200 rows submitted — rejected by maxItems:200 constraint
- Missing required fields (row_id, asset_group, date, render_seconds, sample_count) — validation error
- Invalid date format (not YYYY-MM-DD) — schema pattern mismatch
- Numeric values outside allowed range (±1,000,000,000,000) — schema bounds error
- Payment not attached or insufficient — x402 payment required error
- All render_seconds or sample_count values are null — correlation cannot be computed

## How this service works

Render jobs — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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 string normalisation report cleaning and standardising the text fields (row_id, asset_group, date) across the supplied rows, and (2) a two-measure correlation report quantifying the statistical relationship between render_seconds and sample_count across all submitted render job records (up to 200 rows). No external lookups or predictions are made — analysis is performed entirely on the supplied data.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/render-jobs-string-normalisation-and-two-measure-correlation-f44e6acf/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)