# Duplicate Observations — Category Frequencies + Adjacent Row Changes

> Duplicate Observations — Category Frequencies + 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 2026-10-19).

Analyzes duplicate observation records by computing category frequencies and detecting adjacent row changes across up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-104?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/duplicate-observations-category-frequencies-adjacent-row-changes-c965ca20
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_QXzqxtQd-CTUG5CGmsWhN

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 duplicate-observations-category-frequencies-adjacent-row-changes-c965ca20 -d '<json body>'
```

Example prompt: Can you run the duplicate observations analysis on my dataset — I have rows with duplicate_rows and total_rows counts across groups like 'group-a' and 'group-b' from October 2026, and I want both the category frequency breakdown and the adjacent row change report for the duplicate_rows field?

## When to prefer this

Choose this endpoint when you have structured duplicate observation records (with duplicate_rows, total_rows, dataset_group, and date fields) and need both a categorical frequency breakdown and an adjacent-row change analysis in a single low-cost call. Prefer it over single-report endpoints when both analytical angles are needed simultaneously. Best suited for data quality workflows, pipeline auditing, or ongoing duplicate monitoring where you want to understand group-level composition and row-to-row variation without any external API calls or predictions.

## Known failure modes

- Empty rows array — minimum 1 item required
- Rows exceed 200 item limit — request will be rejected
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) on any row
- Invalid date format — must match YYYY-MM-DD pattern
- Numeric fields out of allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Invalid field enum value — must be 'duplicate_rows' or 'total_rows'
- Payment failure — 0.01 USDC required per call via x402 protocol

## How this service works

Duplicate observations — Category frequencies + Adjacent row changes: Category frequencies + Adjacent row changes for supplied duplicate observations records (duplicate_rows, total_rows). 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 reports: (1) a category frequency distribution showing how often each dataset_group value appears across the supplied rows, and (2) an adjacent row change report identifying differences between consecutive rows for the selected numeric field (duplicate_rows or total_rows). No external lookups or predictions are made; all analysis runs on the supplied data locally.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/duplicate-observations-category-frequencies-adjacent-row-changes-c965ca20/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)