# Data Reconciliation Analytics: Record Quality Audit + String Normalisation + Grouped Numeric Summary

> Data Reconciliation Analytics: Record Quality Audit + String Normalisation + Grouped Numeric Summary 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 local analytical reports on up to 200 data reconciliation rows: a record quality audit, string normalisation, and a grouped numeric summary of source/target totals.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-194?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-analytics-record-quality-audit-string-7ab56809
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_u_cr_ACk8nkmKS60TMq91

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-analytics-record-quality-audit-string-7ab56809 -d '<json body>'
```

Example prompt: Can you run a full reconciliation analysis on these rows — I need a record quality audit, string normalisation on the group labels, and a grouped numeric summary of source_total across dataset groups? Here are up to 200 rows with row_id, date (YYYY-MM-DD), dataset_group, source_total, and target_total.

## When to prefer this

Choose this endpoint when you need three combined analytical reports on a reconciliation dataset in a single API call without any external data lookups: specifically a quality audit, string normalisation, and grouped numeric summary. It is ideal for finance, data engineering, or ETL validation workflows where you want offline, deterministic analysis of up to 200 rows of source/target reconciliation data at very low cost ($0.01 USDC for all three reports).

## Known failure modes

- Rows array empty or missing — returns validation error
- More than 200 rows submitted — rejected with max items error
- date field does not match YYYY-MM-DD pattern — schema validation failure
- row_id exceeds 80 characters or is empty — rejected
- source_total or target_total outside ±1,000,000,000,000 range — rejected
- Invalid field enum value (not 'source_total' or 'target_total') — rejected

## How this service works

Data reconciliation — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary 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

Returns three independent analytical reports: (1) a record quality audit flagging null values, format violations, and completeness issues per row; (2) a string normalisation report cleaning and standardising the dataset_group and other string fields; and (3) a grouped numeric summary aggregating source_total or target_total values by dataset_group, with counts and statistics. All analysis is performed locally on 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-analytics-record-quality-audit-string-7ab56809/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)