# Dataset Partition Sizes — Adjacent Row Changes + Date by Category Pivot Analytics

> Dataset Partition Sizes — Adjacent Row Changes + Date by Category Pivot Analytics 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).

Runs two analytical reports on supplied dataset partition size records: adjacent row-over-row change detection and a date-by-category pivot, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-07-160?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/dataset-partition-sizes-adjacent-row-changes-date-by-category-pivot-504fabab
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_LcYXPUJD65szc6kUI4Jnl

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 dataset-partition-sizes-adjacent-row-changes-date-by-category-pivot-504fabab -d '<json body>'
```

Example prompt: I have a list of dataset partition records with row_id, dataset_group, date, row_count, and file_mb — can you run the adjacent row changes and date-by-category pivot reports on the row_count field for up to 200 rows?

## When to prefer this

Choose this endpoint when you have raw dataset partition size records and need two specific analytical views — sequential row-to-row change detection and a date-by-category pivot — in a single low-cost call. It is ideal for data engineers or pipelines monitoring partition growth trends without needing external data sources or predictive models. Best for structured tabular inputs of up to 200 rows with date, group, and numeric size fields.

## Known failure modes

- Missing required fields (row_id, dataset_group, date, row_count, file_mb) cause validation errors
- Date strings not matching YYYY-MM-DD pattern are rejected
- Input arrays exceeding 200 rows are rejected
- Null numeric values in the selected field may produce incomplete or skipped change calculations
- Invalid field enum value (not 'row_count' or 'file_mb') causes a schema error
- Empty rows array (minItems: 1) is rejected

## How this service works

Dataset partition sizes — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot for supplied dataset partition sizes records (row_count, file_mb). 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 adjacent row changes report showing differences in the selected metric (row_count or file_mb) between consecutive records, and (2) a date-by-category pivot table breaking out the metric values by date and dataset_group — all computed locally from the supplied data, no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-partition-sizes-adjacent-row-changes-date-by-category-pivot-504fabab/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)