# Duplicate Observations Analytics — Record Quality Audit + String Normalisation + Category Frequencies

> Duplicate Observations Analytics — Record Quality Audit + 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 2026-10-15).

Runs three local analytical reports (record quality audit, string normalisation, and category frequencies) on up to 200 rows of duplicate-observation data (duplicate_rows, total_rows) without any external lookups or predictions.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-195?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/duplicate-observations-analytics-record-quality-audit-string-a24179a5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_pNRifLgVdjP6_lL2leOsz

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-a24179a5 -d '<json body>'
```

Example prompt: Can you run a full quality audit on my duplicate observations data — I have 4 rows for groups 'group-a' and 'group-b' dated 2026-10-01 through 2026-10-04 with duplicate_rows values of 10, 20, 15, and 30 against total_rows of 1, 2, 3, and 4 — and give me the record quality audit, string normalisation, and category frequency reports all at once?

## When to prefer this

Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, category frequencies) on duplicate-observation records in a single low-cost call. It is ideal for batch profiling up to 200 rows of deduplification tracking data without any external data lookups, predictions, or live integrations. Prefer it over single-report alternatives when you need all three report types simultaneously on the same dataset to save on per-call costs.

## Known failure modes

- Fewer than 1 row supplied — request rejected (minItems: 1)
- More than 200 rows supplied — request rejected (maxItems: 200)
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) — schema validation error
- row_id exceeds 80 characters — schema validation error
- Payment of 0.01 USDC not fulfilled — 402 Payment Required response
- Numeric values outside allowed range (±1,000,000,000,000) — schema validation error

## How this service works

Duplicate observations — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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 structured analytical reports returned together: (1) a record quality audit flagging missing, null, or out-of-range values across the supplied rows; (2) a string normalisation report identifying inconsistencies and suggested normalised forms for text fields like dataset_group; and (3) a category frequency table showing the distribution and counts of each unique dataset_group value across the supplied rows.

## 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-a24179a5/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)