# Dataset Storage Analytics: Record Quality Audit + String Normalisation + Base-100 Series

> Dataset Storage Analytics: Record Quality Audit + String Normalisation + Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).

Runs three analytical reports on up to 200 dataset storage records — a record quality audit, string normalisation, and a base-100 index series — for supplied used_mb and row_count data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-201?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-storage-analytics-record-quality-audit-string-normalisation-fda7d7ed
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_62CFU4EYk2NE3O5I5ipzA

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-fda7d7ed -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and base-100 index series on my dataset storage rows? I have 4 rows across groups 'group-a' and 'group-b' from 2026-10-01 to 2026-10-04 with used_mb values, and I want the base-100 series indexed from row 0 on the used_mb field.

## When to prefer this

Choose this endpoint when you have up to 200 dataset storage records and need all three of a quality audit, string normalisation, and base-100 series in a single low-cost call ($0.01 USDC). It is a self-contained, local analytical service — ideal when you do not need live data lookups, predictions, or external enrichment, and want a bundled multi-report output for storage metrics (used_mb, row_count) across named dataset groups.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, dataset_group, used_mb, or row_count) — validation error
- date field does not match YYYY-MM-DD pattern — schema validation failure
- base_index out of range for the supplied row count — index error
- All null values in the selected numeric field — base-100 series cannot be computed
- Malformed JSON body — request parse error

## How this service works

Dataset storage — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series 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 the supplied dataset storage rows: (1) a record quality audit flagging completeness and anomalies, (2) a string normalisation report for text fields like dataset_group, and (3) a base-100 index series for the chosen numeric field (used_mb or row_count) anchored at the specified base_index row. 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",
     "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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-storage-analytics-record-quality-audit-string-normalisation-fda7d7ed/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)