# Duplicate Observations Data Quality Analyzer

> Duplicate Observations Data Quality Analyzer 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).

Runs record quality audit, string normalization, and exact row deduplication analysis on supplied duplicate-observation records, returning three independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-191?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-data-quality-analyzer-82f8fd7a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_9_ckqyRokoVDY1WkwCpuy

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-data-quality-analyzer-82f8fd7a -d '<json body>'
```

Example prompt: Can you run a full data quality audit on my duplicate observations dataset — it has 4 rows with dataset groups 'alpha' and 'beta', dates from 2026-10-01 to 2026-10-04, and duplicate_rows counts of 10, 20, 15, and 30 against total_rows of 1, 2, 3, and 4? I want the record quality audit, string normalization, and exact deduplication reports.

## When to prefer this

Choose this endpoint when you have up to 200 structured duplicate-observation records with row_id, dataset_group, date, duplicate_rows, and total_rows fields and need all three of: record quality auditing, string normalization, and exact deduplication in a single low-cost call ($0.01 USDC). Prefer this over general-purpose data tools when you specifically need a combined deduplication + normalization + quality audit report computed locally without any external lookups or live data connections.

## Known failure modes

- More than 200 rows submitted — exceeds the maxItems:200 limit, request will be rejected
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) cause validation errors
- Date field not matching YYYY-MM-DD pattern causes schema validation failure
- Numeric fields outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) cause validation errors
- Empty rows array (minItems:1 not satisfied) returns an error

## How this service works

Duplicate observations — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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

Three independent analytical reports covering: (1) a record quality audit highlighting data issues in the supplied rows, (2) a string normalization report identifying and correcting inconsistent string fields such as dataset_group, and (3) an exact row deduplication report identifying identical rows among the supplied records — all computed locally without external lookups.

## 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-data-quality-analyzer-82f8fd7a/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)