# Data Reconciliation — String Normalisation + Adjacent Row Changes

> Data Reconciliation — String Normalisation + 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 up to 200 data reconciliation rows to produce string normalization and adjacent row change reports on source/target totals.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-029?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-string-normalisation-adjacent-row-changes-7a30cbc1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_T0LgVWLnipJ3oeftXgwRq

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-string-normalisation-adjacent-row-changes-7a30cbc1 -d '<json body>'
```

Example prompt: Can you run string normalization and adjacent row change analysis on my reconciliation data? I have 4 rows across groups 'group-a' and 'group-b' from 2026-10-01 to 2026-10-04, with source totals 10, 20, 15, 30 and target totals 1, 2, 3, 4 — analyze the source_total field.

## When to prefer this

Choose this endpoint when you need two bundled local analytics reports — string normalization and adjacent row change detection — on a reconciliation dataset of up to 200 rows with source and target totals, without requiring any live data lookups or external enrichment. It is cost-effective at $0.01 USDC for both reports and is ideal for ETL validation, finance close processes, or data quality audits where you already have the source and target figures in hand.

## Known failure modes

- Rows array empty or missing — minItems:1 not met
- More than 200 rows submitted — maxItems:200 exceeded
- Invalid date format not matching YYYY-MM-DD pattern
- source_total or target_total outside ±1,000,000,000,000 range
- Missing required fields (row_id, dataset_group, date) on any row
- Invalid field enum value — must be 'source_total' or 'target_total'
- Payment failure or insufficient USDC balance

## How this service works

Data reconciliation — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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: (1) a string normalization report for the specified field across all supplied rows, and (2) an adjacent row changes report showing sequential differences between consecutive rows, all computed locally without external data lookup.

## 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-string-normalisation-adjacent-row-changes-7a30cbc1/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)