# Schema Conformity — Numeric Interval Filter + IQR Outlier Flags

> Schema Conformity — Numeric Interval Filter + 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).

Filters schema conformity records by a numeric interval and flags IQR outliers for valid_records or checked_records fields across up to 200 rows, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-02-130?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/schema-conformity-numeric-interval-filter-iqr-outlier-flags-d9b25372
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6VpUU_i8DRAT0US1Aaym0

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-numeric-interval-filter-iqr-outlier-flags-d9b25372 -d '<json body>'
```

Example prompt: Check my schema conformity data for outliers — I have up to 50 rows with valid_records and checked_records fields per dataset group per day, and I want you to filter valid_records between 0 and 500 and flag any IQR outliers in that field.

## When to prefer this

Choose this endpoint when you need both range-based filtering and IQR-based outlier detection on schema conformity metrics (valid_records or checked_records) in a single low-cost call. It is ideal for data quality monitoring pipelines where you have up to 200 pre-aggregated conformity rows and want two complementary statistical views without any live external data access. Prefer alternatives if you need more than 200 rows, require real-time data fetching, or need outlier methods beyond IQR (e.g. z-score, DBSCAN).

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row rejection
- minimum greater than maximum produces empty interval filter results
- All null values in the selected field may prevent IQR computation
- Missing required fields (row_id, dataset_group, date) cause schema validation errors
- Field enum value not in ['valid_records', 'checked_records'] causes rejection

## How this service works

Schema conformity — Numeric interval filter + IQR outlier flags: Numeric interval filter + 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 numeric interval filter report showing which rows fall within the specified min/max range for the chosen field, and (2) an IQR outlier flag report identifying rows whose field values are statistical outliers based on interquartile range analysis. Both reports operate on the same supplied rows (up to 200) without any external data lookups.

## 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"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/schema-conformity-numeric-interval-filter-iqr-outlier-flags-d9b25372/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)