# Data Freshness Analytics: Record Quality Audit + String Normalisation + Category Frequencies

> Data Freshness 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 2026-10-19).

Runs three local analytical reports — record quality audit, string normalisation, and category frequencies — on up to 200 rows of data freshness records containing age_minutes and record_count fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-195?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-string-normalisation-65f296fa
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_4gY_rsKQ66RL4AL0KmICR

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-string-normalisation-65f296fa -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and category frequency analysis on my data freshness logs? Here are up to 200 rows with fields like row_id, dataset_group, date, age_minutes, and record_count — I want all three reports in one call.

## When to prefer this

Choose this endpoint when you need all three of record quality auditing, string normalisation, and category frequency analysis on data freshness records in a single low-cost call ($0.01 USDC). It is ideal for ETL monitoring, data observability pipelines, or pre-publish data checks where you have up to 200 rows with age_minutes and record_count fields and want bundled local analytics without any external API calls or live lookups.

## Known failure modes

- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- age_minutes or record_count outside allowed numeric range — error
- Empty rows array — minimum 1 item required

## How this service works

Data freshness — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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 analytical reports: (1) a record quality audit flagging missing, null, or out-of-range values in age_minutes and record_count; (2) a string normalisation report standardizing dataset_group and other string fields; and (3) a category frequency table showing how often each dataset_group value appears across the submitted rows. All computed locally with no external lookups.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-freshness-analytics-record-quality-audit-string-normalisation-65f296fa/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)