# Data Reconciliation — Record Quality Audit + String Normalisation + Duplicate Key Report

> Data Reconciliation — Record Quality Audit + String Normalisation + Duplicate Key Report 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).

Runs three analytical reports (record quality audit, string normalisation, and duplicate key detection) on up to 200 supplied data reconciliation rows comparing source and target totals.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-192?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-string-normalisation-5956880a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7l8ibYm6XEWH4dZQf1_Rl

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-string-normalisation-5956880a -d '<json body>'
```

Example prompt: Can you run the three-report reconciliation check on this batch — record quality audit, string normalisation, and duplicate key detection — for these 12 rows where source_total and target_total come from our October billing pipeline (dataset_group 'billing-oct')?

## When to prefer this

Choose this endpoint when you need three simultaneous analytical reports (quality audit, string normalisation, and duplicate key detection) on a batch of reconciliation records in a single call. It is ideal for ETL validation workflows, financial reconciliation pipelines, and data migration audits where source and target totals must be compared across up to 200 rows. It does not perform live lookups, predictions, or external actions — use it for offline, supplied-data analysis only.

## Known failure modes

- More than 200 rows submitted — request rejected with validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) — row-level validation failure
- Date not in YYYY-MM-DD format — schema validation error
- Numeric totals outside allowed range (±1,000,000,000,000) — validation rejection
- Empty rows array — schema minimum items error
- Payment failure or insufficient USDC balance — 402 Payment Required

## How this service works

Data reconciliation — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report for supplied data reconciliation records (source_total, target_total). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports: (1) a record quality audit flagging missing, malformed, or out-of-range values; (2) a string normalisation report identifying and correcting inconsistencies in text fields like dataset_group; and (3) a duplicate key report listing any row_id values that appear more than once across the submitted rows.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-record-quality-audit-string-normalisation-5956880a/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)