# Duplicate Observations — Record Quality Audit, String Normalisation & Duplicate Key Report

> Duplicate Observations — 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 2026-10-20).

Analyzes supplied duplicate-observation records to produce three local analytical reports: a record quality audit, string normalization output, and a duplicate key report — all without external lookups or live data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-192?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/duplicate-observations-record-quality-audit-string-normalisation-b4f8862b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_EVLhTCS85wnv4h13xoMuT

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 duplicate-observations-record-quality-audit-string-normalisation-b4f8862b -d '<json body>'
```

Example prompt: Run a record quality audit, string normalization, and duplicate key report on my observation dataset — I have 4 rows: row-1 for group-a on 2026-10-01 with 10 duplicates out of 1 total, row-2 for group-b on 2026-10-02 with 20 duplicates out of 2 total, row-3 for group-a on 2026-10-03 with 15 duplicates out of 3 total, and row-4 for group-b on 2026-10-04 with 30 duplicates out of 4 total.

## When to prefer this

Choose this endpoint when you have up to 200 pre-collected observation records with duplicate counts and need three bundled analytical reports (quality audit, string normalization, duplicate key detection) in a single low-cost call. It is ideal for lightweight, offline-style data quality checks on batch observation data where no live external lookup, prediction, or professional assessment is needed. It is not suitable for datasets exceeding 200 rows or for use cases requiring real-time streaming analysis or ML-based anomaly detection.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) causes validation errors
- date field not matching YYYY-MM-DD pattern causes schema rejection
- duplicate_rows or total_rows values outside allowed numeric range cause errors
- Empty rows array (minItems: 1 violated) causes rejection
- Payment not fulfilled — x402 payment required before processing

## How this service works

Duplicate observations — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report for supplied duplicate observations records (duplicate_rows, total_rows). 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 reports for the submitted rows (up to 200): (1) a record quality audit flagging data integrity issues, (2) a string normalization report cleaning and standardizing text fields like dataset_group, and (3) a duplicate key report identifying rows with matching or suspicious key combinations — all computed locally with no external calls.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "duplicate_rows",
     "total_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "total_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "duplicate_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "total_rows": 1,
     "dataset_group": "group-a",
     "duplicate_rows": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "total_rows": 2,
     "dataset_group": "group-b",
     "duplicate_rows": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "total_rows": 3,
     "dataset_group": "group-a",
     "duplicate_rows": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "total_rows": 4,
     "dataset_group": "group-b",
     "duplicate_rows": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/duplicate-observations-record-quality-audit-string-normalisation-b4f8862b/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)