# Data Freshness Analytics: Record Quality Audit, Exact Row Deduplication & Numeric Distribution

> Data Freshness Analytics: Record Quality Audit, Exact Row Deduplication & Numeric Distribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Runs three independent analytical reports (record quality audit, exact row deduplication, and numeric distribution) on supplied data freshness records containing age_minutes and record_count fields, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-210?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-freshness-analytics-record-quality-audit-exact-row-deduplication-c5cfd075
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1WVo-N9zRT5fBqizqen_K

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 data-freshness-analytics-record-quality-audit-exact-row-deduplication-c5cfd075 -d '<json body>'
```

Example prompt: Can you run a record quality audit, exact row deduplication check, and numeric distribution report on my data freshness records? Here are 4 rows: row-1 on 2026-10-01 with age_minutes=10 and record_count=1 in group-a, row-2 on 2026-10-02 with age_minutes=20 and record_count=2 in group-b, row-3 on 2026-10-03 with age_minutes=15 and record_count=3 in group-a, row-4 on 2026-10-04 with age_minutes=30 and record_count=4 in group-b — analyze the age_minutes field for the distribution report.

## When to prefer this

Choose this endpoint when you need three simultaneous analytical reports — quality audit, deduplication, and numeric distribution — on data freshness records with age_minutes and record_count fields, without requiring any external data sources or live lookups. It is ideal for lightweight, offline profiling of small-to-medium tabular datasets (up to 200 rows) at low cost ($0.01 USDC for all three reports). Prefer it over general-purpose analytics platforms when you need a self-contained, pay-per-call audit with no infrastructure setup.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) in any row
- Invalid date format (must match YYYY-MM-DD)
- Invalid field enum value (must be age_minutes or record_count)
- Empty rows array (minItems is 1)
- row_id or dataset_group strings exceeding 80 characters
- Numeric values outside allowed range (-1000000000000 to 1000000000000)

## How this service works

Data freshness — Record quality audit + Exact row deduplication + Numeric distribution: Record quality audit + Exact row deduplication + Numeric distribution for supplied data freshness records (age_minutes, record_count). 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 reports: (1) a record quality audit highlighting issues such as nulls, invalid values, or malformed fields; (2) an exact row deduplication report identifying fully identical rows; and (3) a numeric distribution report (min, max, mean, percentiles, etc.) for the selected numeric field (age_minutes or record_count). All analysis is performed locally on the supplied rows — no external lookups or predictions are made.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "age_minutes",
     "record_count"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "age_minutes": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "record_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "age_minutes": 10,
     "record_count": 1,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "age_minutes": 20,
     "record_count": 2,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "age_minutes": 15,
     "record_count": 3,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "age_minutes": 30,
     "record_count": 4,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "age_minutes",
    "record_count"
   ],
   "type": "string",
   "default": "age_minutes"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-freshness-analytics-record-quality-audit-exact-row-deduplication-c5cfd075/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)