# Duplicate Observations Analytics: Record Quality Audit, String Normalisation & Two-Measure Correlation

> Duplicate Observations Analytics: Record Quality Audit, String Normalisation & Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).

Runs three local analytical reports on supplied duplicate-observation records: a record quality audit, string normalisation, and a two-measure correlation between duplicate_rows and total_rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-204?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/duplicate-observations-analytics-record-quality-audit-string-da081cc8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_apE16EgagzVNSYCpq7csm

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-analytics-record-quality-audit-string-da081cc8 -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and correlation analysis on my duplicate observations data? I have 4 rows: row-1 on 2026-10-01 in group-a with 10 duplicate rows out of 1 total, row-2 on 2026-10-02 in group-b with 20 duplicates out of 2 total, row-3 on 2026-10-03 in group-a with 15 duplicates out of 3 total, and row-4 on 2026-10-04 in group-b with 30 duplicates out of 4 total.

## When to prefer this

Choose this endpoint when you need a bundled, cost-effective ($0.01 USDC for all three reports) offline analytical pass over a small batch of duplicate-observation records (up to 200 rows) and require record quality auditing, string field normalisation, and numeric correlation in a single call with no external data dependencies.

## Known failure modes

- Fewer than 1 row submitted — minimum 1 row required
- More than 200 rows submitted — hard cap enforced
- Malformed date strings not matching YYYY-MM-DD pattern
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows)
- Numeric fields outside allowed range (-1e12 to 1e12)
- Payment failure or insufficient USDC balance

## How this service works

Duplicate observations — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation 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 analytical reports for the submitted rows (up to 200): (1) a record quality audit flagging issues such as nulls, out-of-range values, or malformed dates; (2) a string normalisation report highlighting inconsistencies in text fields like dataset_group; and (3) a two-measure correlation report quantifying the relationship between duplicate_rows and total_rows across the supplied records. All analysis is performed locally with no external lookups or predictions.

## 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-analytics-record-quality-audit-string-da081cc8/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)