# Schema Conformity Adjacent Row Changes and IQR Outlier Flags

> Schema Conformity Adjacent Row Changes and 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 2026-10-18).

Analyzes schema conformity time-series data (valid_records, checked_records) to detect adjacent row changes and flag IQR-based outliers across up to 200 rows in two independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-02-157?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/schema-conformity-adjacent-row-changes-and-iqr-outlier-flags-a56f0f8e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_v7VY9_iK7BRyiJhT-XxzZ

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-adjacent-row-changes-and-iqr-outlier-flags-a56f0f8e -d '<json body>'
```

Example prompt: I have 50 rows of daily schema conformity data with valid_records and checked_records columns — can you run adjacent row change detection and IQR outlier flagging on the valid_records field to spot any anomalies?

## When to prefer this

Choose this endpoint when you need fast, local statistical analysis of schema conformity time-series data specifically — combining adjacent row delta detection with IQR-based outlier flagging in a single call. Prefer it over general-purpose analytics when your data already has the valid_records/checked_records schema conformity structure and you want two complementary anomaly views at low cost ($0.01 for both reports).

## Known failure modes

- Fewer than 1 row supplied — request rejected
- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, date, valid_records, checked_records) on any row — validation error
- Date format not matching YYYY-MM-DD pattern — schema rejection
- Invalid field enum value other than valid_records or checked_records — rejected
- All values null — IQR computation may be undefined or empty

## How this service works

Schema conformity — Adjacent row changes + IQR outlier flags: Adjacent row changes + 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) an adjacent row change report showing differences between consecutive rows for the selected field, and (2) an IQR outlier flag report identifying rows whose values fall outside the interquartile range boundary. Both reports are computed locally from the supplied data with no external 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/schema-conformity-adjacent-row-changes-and-iqr-outlier-flags-a56f0f8e/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)