# Dataset Storage IQR Outlier Flags + Date by Category Pivot Analytics

> Dataset Storage IQR Outlier Flags + Date by Category Pivot Analytics is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Runs two local analytical reports on submitted dataset storage records: IQR-based outlier flagging and a date-by-category pivot table, for up to 200 rows of used_mb or row_count data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-178?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-storage-iqr-outlier-flags-date-by-category-pivot-analytics-646f0cd0
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_574dzKWodLAsOvpdaDvhv

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-iqr-outlier-flags-date-by-category-pivot-analytics-646f0cd0 -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and a date-by-category pivot on this storage dataset — field is used_mb — here are my rows: row-1, group-a, 2026-10-01, 10 MB, 1 row; row-2, group-b, 2026-10-02, 20 MB, 2 rows; row-3, group-a, 2026-10-03, 15 MB, 3 rows; row-4, group-b, 2026-10-04, 300 MB, 4 rows?

## When to prefer this

Choose this endpoint when you have up to 200 dataset storage records (with used_mb and row_count fields) and need both IQR outlier detection and a date-by-category pivot in a single low-cost call. It is ideal for offline/batch analytical passes over collected storage metrics without requiring a live database connection or external data enrichment. Prefer this over general-purpose analytics tools when cost predictability ($0.01 flat for both reports) and no-side-effect local computation matter.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — request rejected
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) on any row
- Date values not matching YYYY-MM-DD format — validation error
- Invalid field enum value (must be 'used_mb' or 'row_count')
- All values null for the selected metric — IQR computation undefined
- Payment not included or insufficient — 402 response

## How this service works

Dataset storage — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot for supplied dataset storage records (used_mb, row_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Returns two analytical reports: (1) each input row annotated with an IQR-based outlier flag indicating whether used_mb or row_count is statistically anomalous, and (2) a pivot table summarizing the chosen metric (used_mb or row_count) broken down by date and dataset_group category. No external data is fetched; all computation is local.

## 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-iqr-outlier-flags-date-by-category-pivot-analytics-646f0cd0/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)