# Duplicate Observations Numeric Distribution and Adjacent Row Changes Analyzer

> Duplicate Observations Numeric Distribution and Adjacent Row Changes Analyzer is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).

Analyzes up to 200 supplied duplicate-observation records, producing two independent reports: a numeric distribution analysis and an adjacent row change analysis for duplicate_rows or total_rows fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-077?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/duplicate-observations-numeric-distribution-and-adjacent-row-changes-a28f4997
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6xay6jDol-OSa8T8QwzdM

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-numeric-distribution-and-adjacent-row-changes-a28f4997 -d '<json body>'
```

Example prompt: Can you run a numeric distribution and adjacent row changes analysis on my duplicate observation records? I have rows with dataset_group, date, duplicate_rows, and total_rows — here are up to 50 rows, and I want the analysis focused on the duplicate_rows field.

## When to prefer this

Choose this endpoint when you have pre-collected duplicate observation records and need two complementary offline analytical views — numeric distribution and adjacent row changes — in a single low-cost call. It is ideal for data quality pipelines, ingestion monitoring, and audit workflows where the data is already available locally and no live lookup or prediction is required. At $0.01 USDC for both reports on up to 200 rows, it is cost-effective for batch profiling tasks.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — request rejected
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Field selector not one of 'duplicate_rows' or 'total_rows' causes enum validation failure
- Null values in the selected numeric field may affect distribution completeness
- Values outside the min/max numeric bounds cause schema rejection

## How this service works

Duplicate observations — Numeric distribution + Adjacent row changes: Numeric distribution + 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 analytical reports for the submitted rows: (1) a numeric distribution report showing statistical spread, min, max, mean, and distribution characteristics of the selected field (duplicate_rows or total_rows), and (2) an adjacent row changes report quantifying the delta between consecutive rows in the ordered dataset. All analysis is performed locally on the supplied data with no external lookups.

## 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-numeric-distribution-and-adjacent-row-changes-a28f4997/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)