# Data Reconciliation: Record Quality Audit + Adjacent Row Changes

> Data Reconciliation: Record Quality Audit + 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).

Audits data reconciliation records for quality issues and detects adjacent row changes across source and target totals, producing two independent analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-011?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-record-quality-audit-adjacent-row-changes-d9c08ce6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_8e-7l_MFQumQpr8tqzwr-

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-record-quality-audit-adjacent-row-changes-d9c08ce6 -d '<json body>'
```

Example prompt: Audit the quality of these reconciliation records and flag any adjacent row changes — here are 4 rows: row-1 on 2026-10-01 in group-a with source_total 10 and target_total 1, row-2 on 2026-10-02 in group-b with source_total 20 and target_total 2, row-3 on 2026-10-03 in group-a with source_total 15 and target_total 3, and row-4 on 2026-10-04 in group-b with source_total 30 and target_total 4; analyze the source_total field.

## When to prefer this

Choose this endpoint when you have reconciliation records with source and target totals and need two specific analytical reports — a record quality audit and adjacent row change detection — without requiring any external data lookups or live systems. It is cost-effective at $0.01 for both reports on up to 200 rows, making it ideal for batch ETL validation, finance reconciliation pipelines, or post-deployment regression checks on data quality.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) causes rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Numeric values outside [-1,000,000,000,000, 1,000,000,000,000] are rejected
- Empty rows array (minItems: 1 not met) causes error
- Invalid field enum value (not source_total or target_total) causes rejection

## How this service works

Data reconciliation — Record quality audit + Adjacent row changes: Record quality audit + 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 record quality audit identifying data quality issues in the supplied reconciliation rows, and (2) an adjacent row changes report highlighting where consecutive rows differ significantly. Both reports are generated locally from 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",
     "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-record-quality-audit-adjacent-row-changes-d9c08ce6/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)