# Dataset Storage Analytics — Record Quality Audit, String Normalisation & Date-by-Category Pivot

> Dataset Storage Analytics — Record Quality Audit, String Normalisation & Date-by-Category Pivot 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 (record quality audit, string normalisation, and date-by-category pivot) on up to 200 rows of dataset storage records containing used_mb and row_count fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-206?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-92cbd94b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_cYUDEelEZcuLZZ8TMCulS

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-92cbd94b -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and date-by-category pivot on my storage dataset? I have 4 rows: row-1 on 2026-10-01 with used_mb=10 and row_count=1 in group-a, row-2 on 2026-10-02 with used_mb=20 and row_count=2 in group-b, row-3 on 2026-10-03 with used_mb=15 and row_count=3 in group-a, and row-4 on 2026-10-04 with used_mb=30 and row_count=4 in group-b — use used_mb as the analysis field.

## When to prefer this

Choose this endpoint when you need three simultaneous analytical passes — quality auditing, string normalisation, and categorical date pivoting — on a batch of up to 200 dataset storage records in a single $0.01 call. It is best suited for data engineering pipelines that need quick, offline assessment of storage metrics (used_mb, row_count) without any external enrichment or live data lookups. Prefer alternatives if you need more than 200 rows, real-time data, or analytics on fields beyond used_mb and row_count.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, used_mb, or row_count) — validation error
- Invalid date format (not YYYY-MM-DD) — schema pattern validation failure
- field parameter set to a value other than 'used_mb' or 'row_count' — enum validation error
- Payment of 0.01 USDC not completed — request blocked by x402 payment requirement

## How this service works

Dataset storage — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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 returned together: (1) a record quality audit highlighting missing, null, or anomalous values in the supplied rows; (2) a string normalisation report on text fields such as dataset_group and row_id; and (3) a date-by-category pivot summarising the selected numeric field (used_mb or row_count) broken down by date and dataset_group. All reports are computed locally with no external data lookups.

## 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
  },
  "field": {
   "enum": [
    "used_mb",
    "row_count"
   ],
   "type": "string",
   "default": "used_mb"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-storage-analytics-record-quality-audit-string-normalisation-92cbd94b/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)