# Transformation Throughput Analytics — String Normalisation & Adjacent Row Changes

> Transformation Throughput Analytics — String Normalisation & Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).

Analyzes transformation throughput records to produce string normalisation and adjacent row change reports for up to 200 rows of processed_rows and duration_seconds data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-029?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transformation-throughput-analytics-string-normalisation-adjacent-row-e2a3bca1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7PSX7Lh6XOsG9bwhUHJRR

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-analytics-string-normalisation-adjacent-row-e2a3bca1 -d '<json body>'
```

Example prompt: Can you run a string normalisation and adjacent row changes analysis on my transformation throughput data? I have 4 rows for group-a and group-b from October 2026, with processed_rows values of 10, 20, 15, 30 and duration_seconds of 1, 2, 3, 4 — use processed_rows as the field.

## When to prefer this

Choose this endpoint when you need both string normalisation and adjacent row change detection on transformation throughput records in a single call for up to 200 rows. It is ideal for ETL pipeline auditing, data quality checks before handoffs, and identifying consecutive row anomalies in processed_rows or duration_seconds without requiring external lookups or live data connections.

## Known failure modes

- Missing required fields (row_id, date, dataset_group, processed_rows, duration_seconds) causes validation error
- More than 200 rows submitted exceeds the per-call limit and is rejected
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Null or out-of-range numeric values (beyond ±1,000,000,000,000) may cause computation errors
- Invalid field enum value (anything other than processed_rows or duration_seconds) returns an error
- Payment of 0.01 USDC not included causes x402 payment required response

## How this service works

Transformation throughput — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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 independent analytical reports: (1) a string normalisation report that standardizes and cleans string fields across the supplied rows, and (2) an adjacent row changes report that identifies value differences between consecutive rows for the selected numeric field (processed_rows or duration_seconds), all computed locally without external lookups.

## 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
  },
  "field": {
   "enum": [
    "processed_rows",
    "duration_seconds"
   ],
   "type": "string",
   "default": "processed_rows"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/transformation-throughput-analytics-string-normalisation-adjacent-row-e2a3bca1/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)