# Duplicate Observations Record Quality Audit and String Normalisation

> Duplicate Observations Record Quality Audit and String Normalisation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).

Audits and normalises up to 200 rows of duplicate-observation records, producing a record quality report and a string normalisation report using local analytical engines.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-001?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/duplicate-observations-record-quality-audit-and-string-normalisation-dc956e37
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_GL8ibMdGd4PsuzcAk-tOd

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-and-string-normalisation-dc956e37 -d '<json body>'
```

Example prompt: Can you run a record quality audit and string normalisation check on these 4 rows of duplicate observation data — row-1 for group-a on 2026-10-01 with 10 duplicate_rows out of 1 total, row-2 for group-b on 2026-10-02 with 20 duplicate_rows out of 2 total, row-3 for group-a on 2026-10-03 with 15 duplicate_rows out of 3 total, and row-4 for group-b on 2026-10-04 with 30 duplicate_rows out of 4 total?

## When to prefer this

Choose this endpoint when you have up to 200 rows of pre-collected duplicate observation records (duplicate_rows, total_rows, dataset_group, date) and need both a structured record quality audit and a string normalisation report in a single low-cost call. It is ideal for automated data pipeline checkpoints, pre-load validation steps, or periodic data quality reviews where no live external lookups, predictions, or professional assessments are required. Prefer alternatives if you need null coverage analysis, trend analysis, or correlation reports — those are covered by sibling endpoints on the same platform.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- duplicate_rows or total_rows outside numeric bounds — rejection
- Empty rows array (minItems:1 not met) — error returned
- Payment of 0.01 USDC not completed — 402 response blocking execution

## How this service works

Duplicate observations — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied duplicate observations records (duplicate_rows, total_rows). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Returns two independent reports: (1) a record quality audit assessing the integrity, completeness, and plausibility of the supplied duplicate_rows and total_rows figures across all input rows; (2) a string normalisation report identifying and correcting inconsistencies in text fields such as dataset_group and date. All analysis is performed locally with no external lookups or live data access.

## 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-and-string-normalisation-dc956e37/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)