# Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Trailing Mean Series

> Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Trailing Mean Series 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 three local analytical reports (record quality audit, string normalisation, and trailing mean series) on up to 200 rows of duplicate-observation data containing duplicate_rows and total_rows counts.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-199?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-analytics-record-quality-audit-string-ea8a0dd2
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_lX_ZBrT-L36NKd9uFrrOD

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-ea8a0dd2 -d '<json body>'
```

Example prompt: Can you run the three analytics reports — record quality audit, string normalisation, and trailing mean series — on my duplicate observations data? I have 4 rows for groups 'group-a' and 'group-b' from 2026-10-01 to 2026-10-04, with duplicate_rows values of 10, 20, 15, and 30 against total_rows of 1, 2, 3, and 4. Use 'duplicate_rows' as the field and a window of 2.

## When to prefer this

Choose this endpoint when you need three simultaneous analytical reports on duplicate-observation data in a single low-cost call — specifically record quality auditing, string normalisation of group labels, and a trailing moving average — without requiring any external lookups, predictions, or live data. It is best suited for data engineers auditing ETL pipeline output, analysts reviewing deduplication metrics over time, or automated agents performing scheduled data quality checks on batch record sets of up to 200 rows.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit and will be rejected
- Invalid date format not matching YYYY-MM-DD pattern
- Window value outside the 2–200 integer range
- Numeric values for duplicate_rows or total_rows outside the allowed ±1,000,000,000,000 range
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) on any row
- Invalid field enum — must be exactly 'duplicate_rows' or 'total_rows'
- Payment failure via x402 protocol preventing execution

## How this service works

Duplicate observations — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series 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: (1) a record quality audit flagging issues in the supplied rows, (2) a string normalisation report identifying and correcting inconsistencies in string fields like dataset_group, and (3) a trailing mean series computed over the chosen numeric field (duplicate_rows or total_rows) using the specified window size. All reports are computed locally from the supplied data with no 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
  },
  "field": {
   "enum": [
    "duplicate_rows",
    "total_rows"
   ],
   "type": "string",
   "default": "duplicate_rows"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/duplicate-observations-analytics-record-quality-audit-string-ea8a0dd2/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)