# Numeric Precision Comparisons — Adjacent Row Changes + Two-Measure Correlation

> Numeric Precision Comparisons — Adjacent Row Changes + Two-Measure Correlation 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).

Analyzes up to 200 rows of source/transformed numeric pairs, producing two local reports: adjacent row change deltas and two-measure correlation between source_value and transformed_value.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-158?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/numeric-precision-comparisons-adjacent-row-changes-two-measure-5366ef7c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_717iYNoqVzd3uBF7-gRuM

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 numeric-precision-comparisons-adjacent-row-changes-two-measure-5366ef7c -d '<json body>'
```

Example prompt: Can you run adjacent row change analysis and correlation between source_value and transformed_value on my dataset — I have 4 rows: row-1 on 2026-10-01 (source 10, transformed 1), row-2 on 2026-10-02 (source 20, transformed 2), row-3 on 2026-10-03 (source 15, transformed 3), row-4 on 2026-10-04 (source 30, transformed 4), all in group-a — and use source_value as the primary field?

## When to prefer this

Choose this endpoint when you need a lightweight, local statistical analysis of paired numeric columns (source vs transformed) without sending data to an external prediction or ML service. Ideal for auditing ETL pipelines, data quality checks, or transformation validation where you want both row-to-row delta inspection and a correlation report in a single $0.01 call for up to 200 rows.

## Known failure modes

- More than 200 rows submitted — request exceeds maxItems limit
- Missing required fields (row_id, date, source_value, transformed_value, dataset_group) cause validation errors
- Date strings not matching YYYY-MM-DD pattern are rejected
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array (minItems:1 not met) causes failure
- Invalid field enum value (not 'source_value' or 'transformed_value') causes error

## How this service works

Numeric precision comparisons — Adjacent row changes + Two-measure correlation: Adjacent row changes + Two-measure correlation for supplied numeric precision comparisons records (source_value, transformed_value). 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 computed locally from the supplied rows: (1) adjacent row changes showing the delta between consecutive rows for the selected field, and (2) two-measure correlation between source_value and transformed_value across all supplied rows. No external lookups or predictions are made.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-adjacent-row-changes-two-measure-5366ef7c/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)