# Dataset Storage Analytics: Record Quality Audit + String Normalisation + Category Frequencies

> Dataset Storage 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 analytical reports on dataset storage records — record quality audit, string normalisation, and category frequency analysis — for up to 200 rows of used_mb and row_count data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-195?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/dataset-storage-analytics-record-quality-audit-string-normalisation-58ac7f99
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_WWqAA3xtlq0lUHTDUi4YN

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 dataset-storage-analytics-record-quality-audit-string-normalisation-58ac7f99 -d '<json body>'
```

Example prompt: Can you run a quality audit, string normalisation, and category frequency analysis on these dataset storage records I have — they include row IDs, dataset groups like 'group-a' and 'group-b', dates, used_mb values, and row counts for about 50 rows?

## When to prefer this

Choose this endpoint when you need a combined, low-cost batch analysis of dataset storage records covering data quality, string cleanliness, and categorical distribution in one call. It is best suited for offline or near-real-time data auditing workflows where you already hold the row data locally and need structured analytical reports without any external API calls, predictions, or professional assessments.

## Known failure modes

- Exceeds 200-row limit — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) — validation error
- Invalid date format (must be YYYY-MM-DD) — parsing error
- Numeric fields outside allowed range (±1,000,000,000,000) — validation rejection
- Empty rows array — error response
- Payment not included or insufficient — x402 payment required response

## How this service works

Dataset storage — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied dataset storage records (used_mb, row_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

Three independent analytical reports: (1) a record quality audit flagging nulls, invalid values, and anomalies in used_mb and row_count fields; (2) a string normalisation report cleaning and standardising text fields like dataset_group; (3) a category frequency table showing counts and distributions across dataset_group values. All derived solely from the supplied rows — no external lookups or live data.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-storage-analytics-record-quality-audit-string-normalisation-58ac7f99/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)