# Dataset Storage Analytics — Record Quality Audit, String Normalisation & IQR Outlier Flags

> Dataset Storage Analytics — Record Quality Audit, String Normalisation & IQR Outlier Flags 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 (used_mb, row_count): a record quality audit, string normalisation, and IQR-based outlier flagging — up to 200 rows per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-203?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-5233e2ad
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_eq5TCwuJmTryd48sSa_EK

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-5233e2ad -d '<json body>'
```

Example prompt: Can you audit the quality of these dataset storage records, normalize the group name strings, and flag any IQR outliers in the used_mb field? I have 4 rows: row-1 on 2026-10-01 group-a used_mb=10 row_count=1, row-2 on 2026-10-02 group-b used_mb=20 row_count=2, row-3 on 2026-10-03 group-a used_mb=15 row_count=3, row-4 on 2026-10-04 group-b used_mb=30 row_count=4.

## When to prefer this

Choose this endpoint when you need three combined data quality operations — quality audit, string normalisation, and IQR outlier detection — on dataset storage records in a single low-cost call. It is ideal for pre-pipeline data validation, anomaly detection on storage metrics, or cleaning batch exports of up to 200 rows without requiring external lookups, live data, or professional data services.

## Known failure modes

- Exceeding 200 rows causes a validation error — batch must be split
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) cause schema rejection
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside allowed range (-1000000000000 to 1000000000000) are rejected
- Too few rows may produce unreliable IQR outlier detection (IQR needs sufficient spread)
- Payment failure via x402 prevents execution

## How this service works

Dataset storage — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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

Returns three independent analytical reports for up to 200 supplied dataset storage rows: (1) a record quality audit highlighting missing values, type mismatches, and row-level quality issues; (2) string normalisation results for text fields such as dataset_group; and (3) IQR-based outlier flags identifying statistically anomalous values in either used_mb or row_count depending on the selected field.

## 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-5233e2ad/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)