# Dataset Storage Analytics: Record Quality Audit, Exact Row Deduplication & Base-100 Series

> Dataset Storage Analytics: Record Quality Audit, Exact Row Deduplication & 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 2026-10-15).

Runs three analytical reports on supplied dataset storage records (used_mb, row_count): a record quality audit, exact row deduplication, and a base-100 index series — all locally, with no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-218?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-exact-row-f1b33b24
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_MgegYyC3KLoOMS-2bAIBp

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-exact-row-f1b33b24 -d '<json body>'
```

Example prompt: Can you run a record quality audit, exact row deduplication, and base-100 index series on my dataset storage records? Here are up to 200 rows with row_id, dataset_group, date, used_mb, and row_count — analyze the used_mb field and start the index at row 0.

## When to prefer this

Choose this endpoint when you need all three analytical operations — quality audit, deduplication, and base-100 indexing — on the same dataset storage records in a single call for a flat $0.01 USDC. It is ideal for pre-ingestion data validation pipelines, periodic storage reporting, or trend normalization tasks on batches up to 200 rows. Prefer alternatives if you need live data retrieval, predictive analytics, professional assessment, or datasets larger than 200 rows.

## Known failure modes

- Submitting more than 200 rows returns a validation error — batch must be split
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) causes schema validation failure
- base_index out of range (>= number of submitted rows) causes an indexing error
- Non-conforming date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric fields outside allowed range cause schema rejection
- Empty rows array (minItems: 1 not met) returns a validation error

## How this service works

Dataset storage — Record quality audit + Exact row deduplication + Base-100 series: Record quality audit + Exact row deduplication + 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: (1) a record quality audit highlighting missing values, anomalies, or invalid entries in the supplied rows; (2) an exact row deduplication report identifying identical records; and (3) a base-100 indexed series for the selected numeric field (used_mb or row_count), starting from the specified base row index. All computation is local with no external data fetched.

## 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-exact-row-f1b33b24/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)