# Duplicate Observations — String Normalisation + Category Frequencies

> Duplicate Observations — 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 2026-10-20).

Analyzes up to 200 duplicate observation records to produce two reports: string normalisation of text fields and category frequency distributions for dataset groups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-024?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-string-normalisation-category-frequencies-0bda7bfe
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_cGEW7Qq680CFwopmnsQQU

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-string-normalisation-category-frequencies-0bda7bfe -d '<json body>'
```

Example prompt: I have 4 duplicate observation records — can you run string normalization and category frequency analysis on them? Rows are: row-1 on 2026-10-01 in group-a with 10 duplicates 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 have structured duplicate observation data (with row IDs, dates, group labels, duplicate counts, and total counts) and need both text normalization of categorical fields and frequency distribution analysis in a single call. It is ideal for data quality workflows, ETL monitoring, and research deduplication audits where batch processing of up to 200 records is needed without any external lookups or live data fetching.

## Known failure modes

- Fewer than 1 row submitted — minItems constraint violated
- More than 200 rows submitted — maxItems constraint exceeded
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) causes rejection
- Non-numeric values for duplicate_rows or total_rows where numbers are expected
- Values outside allowed numeric range (±1,000,000,000,000) rejected
- Payment not included or insufficient — x402 payment required at $0.01 USDC per call

## How this service works

Duplicate observations — String normalisation + Category frequencies: String normalisation + Category frequencies 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

Two analytical reports for the supplied records: (1) a string normalisation report that standardizes text fields such as dataset_group names, and (2) a category frequency report showing the distribution and counts of duplicate observations across dataset groups, along with duplicate rate metrics derived from duplicate_rows and total_rows fields.

## 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-string-normalisation-category-frequencies-0bda7bfe/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)