# Schema Conformity Grouped Numeric Summary with IQR Outlier Flags

> Schema Conformity Grouped Numeric Summary with 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 2026-10-17).

Computes grouped descriptive statistics and IQR-based outlier flags for schema conformity records (valid_records and checked_records) across up to 200 rows in two independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-02-094?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-17
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/schema-conformity-grouped-numeric-summary-with-iqr-outlier-flags-ff9f0924
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7hmp-PtfVTg8RESLD34sl

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 schema-conformity-grouped-numeric-summary-with-iqr-outlier-flags-ff9f0924 -d '<json body>'
```

Example prompt: Can you run a grouped numeric summary and IQR outlier analysis on my schema conformity data — I have up to 50 rows with dataset_group, date, valid_records, and checked_records columns, and I want two reports focusing on the valid_records field?

## When to prefer this

Choose this endpoint when you have tabular schema conformity data with valid_records and checked_records columns and need both grouped descriptive statistics and IQR-based outlier detection in a single low-cost call. It is ideal for data quality monitoring workflows where you want offline, local analytical computation without external lookups or predictions. Best suited for batches of up to 200 rows where two simultaneous analytical reports are needed cheaply at $0.01 USDC.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, valid_records, checked_records) cause validation errors
- Invalid date format (must match YYYY-MM-DD pattern) triggers schema rejection
- Null values in valid_records or checked_records may affect IQR computation accuracy
- Single-row or single-group datasets may produce degenerate or uninformative IQR results

## How this service works

Schema conformity — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + IQR outlier flags for supplied schema conformity records (valid_records, checked_records). 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 independent analytical reports per call: (1) a grouped numeric summary with descriptive statistics (e.g. mean, median, min, max, count) broken down by dataset_group, and (2) IQR-based outlier flags identifying rows whose values fall outside the interquartile range, for the selected field (valid_records or checked_records) across up to 200 input rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "valid_records",
     "checked_records"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "valid_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "checked_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "valid_records": 10,
     "checked_records": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "valid_records": 20,
     "checked_records": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "valid_records": 15,
     "checked_records": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "valid_records": 30,
     "checked_records": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "valid_records",
    "checked_records"
   ],
   "type": "string",
   "default": "valid_records"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/schema-conformity-grouped-numeric-summary-with-iqr-outlier-flags-ff9f0924/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)