# Transformation Throughput Analytics — Adjacent Row Changes & Secondary-Measure Weighted Mean

> Transformation Throughput Analytics — Adjacent Row Changes & Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Computes adjacent row-to-row changes and a secondary-measure weighted mean for transformation throughput records containing processed_rows and duration_seconds fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-159?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transformation-throughput-analytics-adjacent-row-changes-secondary-791a3d58
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_q_U6pxMkPb8nreqC_mGzA

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-adjacent-row-changes-secondary-791a3d58 -d '<json body>'
```

Example prompt: Can you run two analytical reports on my transformation throughput data — compute the adjacent row changes and the weighted mean of duration_seconds — for these pipeline runs: row-1 on 2026-10-01, group-a, 10 processed_rows, 1 duration_second; row-2 on 2026-10-02, group-b, 20 processed_rows, 2 duration_seconds; row-3 on 2026-10-03, group-a, 15 processed_rows, 3 duration_seconds?

## When to prefer this

Choose this endpoint when you need two specific statistical computations — adjacent sequential differences and a weighted mean — on transformation throughput tabular data (processed_rows, duration_seconds) in a single call. It is ideal for ETL pipeline analysis, batch throughput monitoring, and data engineering quality checks where you have up to 200 dated records and want local analytical results without external data enrichment or prediction.

## Known failure modes

- Missing required fields (row_id, date, processed_rows, duration_seconds) cause validation errors
- More than 200 rows submitted exceeds the max item limit
- Invalid date format (not YYYY-MM-DD) rejected by schema pattern
- Null values in processed_rows or duration_seconds may affect weighted mean computation
- Invalid field enum value (not 'processed_rows' or 'duration_seconds') causes rejection
- Insufficient USDC balance or payment failure blocks the call

## How this service works

Transformation throughput — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean 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

Two analytical reports derived from the supplied throughput records: (1) adjacent row-to-row changes for the specified field (processed_rows or duration_seconds), showing sequential deltas across up to 200 rows; and (2) a secondary-measure weighted mean, where one measure weights the average of the other. Results are computed locally with no external lookups or predictions.

## 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-adjacent-row-changes-secondary-791a3d58/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)