# Data Freshness Analytics: Record Quality Audit + String Normalisation + Trailing Mean Series

> Data Freshness 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 2026-10-19).

Runs three local analytical reports (record quality audit, string normalisation, and trailing mean series) on up to 200 data freshness rows containing age_minutes and record_count fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-199?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-0e983b77
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_wrI2yt0DPUua7HKFJe2xy

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-0e983b77 -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and trailing mean series (window of 3) on my data freshness records for age_minutes? Here are my rows: row-1 on 2026-10-01 with age_minutes=10, record_count=5, group-a; row-2 on 2026-10-02 with age_minutes=20, record_count=8, group-b; row-3 on 2026-10-03 with age_minutes=15, record_count=3, group-a.

## When to prefer this

Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, trailing mean) on data freshness records without any external data sources or live lookups. It is ideal for offline/batch analytical workflows on pipeline metadata, data staleness logs, or ETL freshness tracking where cost efficiency matters ($0.01 for all three reports on up to 200 rows). Prefer alternatives if you need predictions, external data enrichment, or more than 200 rows per call.

## Known failure modes

- More than 200 rows submitted — returns an error as the maximum batch size is exceeded
- Missing required fields (row_id, date, age_minutes, record_count, dataset_group) on any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Window size outside 2–200 range — rejected by schema
- Field value outside allowed numeric range — schema rejection
- Empty rows array — minimum 1 item required

## How this service works

Data freshness — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series 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 identifying data issues in the submitted rows, (2) a string normalisation report for categorical/string fields like dataset_group, and (3) a trailing mean series computed over the selected numeric field (age_minutes or record_count) using the specified window size — all derived purely from the submitted 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",
     "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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-freshness-analytics-record-quality-audit-string-normalisation-0e983b77/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)