# Data Reconciliation Analytics — Record Quality Audit, String Normalisation & Category Frequencies

> Data Reconciliation Analytics — Record Quality Audit, String Normalisation & Category Frequencies 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 supplied data reconciliation rows: a record quality audit, string normalisation, and category frequency analysis — all in a single paid call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-195?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-0292c5e3
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_GamF5NWFJblm-LIBMBOL_

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-0292c5e3 -d '<json body>'
```

Example prompt: Can you run a full quality audit, string normalisation, and category frequency report on these reconciliation records — I have rows with row_id, dataset_group, date, source_total, and target_total fields, up to 200 rows covering groups like 'group-a' and 'group-b' across dates in October 2026?

## When to prefer this

Choose this endpoint when you have up to 200 reconciliation records with source and target totals and need a bundled analytical package covering quality auditing, string normalisation, and category frequency counting in a single low-cost call. It is ideal for data pipeline validation, pre-reporting QA, and categorical summarisation where no external data lookup or prediction is needed.

## Known failure modes

- More than 200 rows submitted — request rejected
- Missing required fields (row_id, dataset_group, date, source_total, target_total) in one or more rows — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric totals outside allowed range (±1,000,000,000,000) — rejected
- Empty rows array — minItems constraint violation
- Payment not included or insufficient — x402 payment required error

## How this service works

Data reconciliation — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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 analytical reports for the supplied rows: (1) a record quality audit flagging nulls, invalid formats, and completeness issues; (2) a string normalisation report standardising dataset_group and other string fields; (3) a category frequency table showing how often each dataset_group value appears. All computed locally 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-analytics-record-quality-audit-string-0292c5e3/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)