# Data Reconciliation: Adjacent Row Changes + IQR Outlier Flags

> Data Reconciliation: 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 2026-10-19).

Analyzes up to 200 data reconciliation rows (source_total vs target_total) and produces two reports: adjacent row-to-row change analysis and IQR-based outlier flagging.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-157?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/data-reconciliation-adjacent-row-changes-iqr-outlier-flags-a0c8b1ed
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_vQCBqLxOdwnDFOR4WHnUv

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 data-reconciliation-adjacent-row-changes-iqr-outlier-flags-a0c8b1ed -d '<json body>'
```

Example prompt: Can you run adjacent row change analysis and IQR outlier flagging on my reconciliation data? I have 28 rows across two dataset groups — 'ledger-A' and 'ledger-B' — with dates in October 2025, and I want to analyze the source_total field.

## When to prefer this

Choose this endpoint when you need lightweight, self-contained statistical analysis of reconciliation record sets — specifically adjacent row delta tracking and IQR-based outlier detection — without requiring external data sources, predictions, or professional assessments. Ideal for data quality checks on financial reconciliation pipelines, ETL validation, and audit workflows where you have up to 200 rows of source/target total pairs and need two complementary anomaly reports in a single low-cost call.

## Known failure modes

- More than 200 rows submitted — request will be rejected
- Missing required fields (row_id, dataset_group, date, source_total, target_total) cause validation error
- Malformed date strings (non-YYYY-MM-DD format) fail schema validation
- Numeric values outside ±1,000,000,000,000 range cause rejection
- Invalid field enum value (not 'source_total' or 'target_total') causes error
- Empty rows array causes rejection

## How this service works

Data reconciliation — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied data reconciliation records (source_total, target_total). 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) adjacent row change analysis showing value-to-value deltas between consecutive rows for the selected field, and (2) IQR (interquartile range) outlier flags identifying which rows have statistically unusual values. Both reports are computed locally with no external data lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-adjacent-row-changes-iqr-outlier-flags-a0c8b1ed/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)