# Transformation Throughput — Exact Row Deduplication + Adjacent Row Changes

> Transformation Throughput — Exact Row Deduplication + 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 2026-10-24).

Runs two local analytical reports on transformation throughput data: exact row deduplication and adjacent row change detection, for up to 200 rows of processed_rows or duration_seconds metrics.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-046?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transformation-throughput-exact-row-deduplication-adjacent-row-changes-4fc7c084
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_QxgqsXonJ2wuCi3XehhLi

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-exact-row-deduplication-adjacent-row-changes-4fc7c084 -d '<json body>'
```

Example prompt: Can you deduplicate these transformation throughput rows and also flag where adjacent rows changed in processed_rows? I have 4 records across group-a and group-b dated 2026-10-01 through 2026-10-04 with processed_rows values of 10, 20, 15, and 30 and duration_seconds of 1, 2, 3, and 4.

## When to prefer this

Choose this endpoint when you need two specific analytical operations on transformation throughput data in a single call: exact duplicate row removal and adjacent/consecutive row change detection. It is ideal for ETL pipeline data quality workflows where you supply your own records (up to 200 rows) and want offline local analysis without any live data lookup, external calls, or prediction. Prefer this over general-purpose analytics when your data specifically has processed_rows and duration_seconds fields grouped by dataset_group and date.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, processed_rows, duration_seconds) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema rejection
- field enum value not in ['processed_rows','duration_seconds'] — invalid input
- Empty rows array — minimum 1 item required
- All rows are identical — deduplication report returns single row, change report shows no changes

## How this service works

Transformation throughput — Exact row deduplication + Adjacent row changes: Exact row deduplication + 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 analytical reports: (1) an exact row deduplication report identifying and removing duplicate records from the supplied throughput data, and (2) an adjacent row changes report showing where consecutive rows differ in the selected metric (processed_rows or duration_seconds). 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",
     "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-exact-row-deduplication-adjacent-row-changes-4fc7c084/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)