# Transformation Throughput — String Normalisation + Two-Measure Correlation

> Transformation Throughput — String Normalisation + 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 2026-10-19).

Normalises string fields and computes two-measure correlation between processed_rows and duration_seconds for up to 200 transformation throughput records, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-033?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transformation-throughput-string-normalisation-two-measure-correlation-d5170af1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_gcfML3-qsUuXs4v4xdiSC

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 transformation-throughput-string-normalisation-two-measure-correlation-d5170af1 -d '<json body>'
```

Example prompt: I have up to 200 rows of pipeline run data with row_id, dataset_group, date, processed_rows, and duration_seconds — can you normalize the string fields and run a two-measure correlation analysis between processed_rows and duration_seconds, giving me two reports?

## When to prefer this

Choose this endpoint when you have a batch of up to 200 transformation throughput records and need both string normalisation and a two-measure statistical correlation between processed_rows and duration_seconds — packaged together in a single low-cost ($0.01 USDC) call with two reports. Prefer this over generic analytics tools when your data is already structured in the expected schema (row_id, dataset_group, date, processed_rows, duration_seconds) and you want lightweight, local, offline computation with no external lookups or side effects.

## Known failure modes

- Missing required fields (row_id, date, processed_rows, duration_seconds) causes validation errors
- Submitting more than 200 rows returns an error due to maxItems constraint
- Null values in processed_rows or duration_seconds may reduce correlation validity
- Improperly formatted dates (not YYYY-MM-DD) will fail pattern validation
- Empty row array rejected by minItems constraint
- Payment failure or insufficient USDC balance prevents the call from completing

## How this service works

Transformation throughput — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied transformation throughput records (processed_rows, duration_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: one covering string normalisation of the dataset_group and related text fields, and one providing a two-measure (Pearson-style) correlation analysis between processed_rows and duration_seconds across the submitted rows, including grouped statistics. No external data is fetched; all computation is performed on the supplied records only.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "processed_rows",
     "duration_seconds"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "processed_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "duration_seconds": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "processed_rows": 10,
     "duration_seconds": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "processed_rows": 20,
     "duration_seconds": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "processed_rows": 15,
     "duration_seconds": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "processed_rows": 30,
     "duration_seconds": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/transformation-throughput-string-normalisation-two-measure-correlation-d5170af1/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)