# Numeric Precision Comparisons — Adjacent Row Changes + IQR Outlier Flags

> Numeric Precision Comparisons — Adjacent Row Changes + IQR Outlier Flags 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 to detect adjacent-row changes and flag IQR-based outliers, producing two independent analytical reports per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-157?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-iqr-outlier-flags-3ffd574d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_2zf5I7NlGIuQc5HM43EH2

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-iqr-outlier-flags-3ffd574d -d '<json body>'
```

Example prompt: Analyze these 50 rows of source and transformed sales figures — field is source_value — and give me both the adjacent row change report and the IQR outlier flags so I can see which rows have unusual jumps or are statistical outliers.

## When to prefer this

Choose this endpoint when you need fast, local (no external data fetched) statistical analysis of numeric paired records — specifically adjacent row change detection AND IQR outlier flagging delivered together in one call for up to 200 rows at very low cost. Ideal for data quality pipelines, ETL validation, and time-series anomaly screening where you control the input data and need two complementary views on numeric variation.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, source_value, transformed_value) — validation error
- date field does not match YYYY-MM-DD pattern — schema validation failure
- source_value or transformed_value out of allowed range (±1 trillion) — rejected
- Payment of 0.01 USDC not received — 402 Payment Required
- Empty rows array — schema minItems violation

## How this service works

Numeric precision comparisons — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags 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

Two independent analytical reports: (1) an adjacent row change report showing delta values between consecutive rows, and (2) an IQR outlier flags report identifying which rows fall outside the interquartile range boundary — all computed locally without external lookups, covering up to 200 input rows.

## 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-iqr-outlier-flags-3ffd574d/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)