# Feature Availability — Adjacent Row Changes + IQR Outlier Flags

> Feature Availability — Adjacent Row Changes + 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 2026-10-20).

Analyzes feature availability records (populated_features, expected_features) to detect adjacent row changes and flag IQR-based outliers across up to 200 rows, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-157?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-adjacent-row-changes-iqr-outlier-flags-50ffe870
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_XP6OKBbQ0X3UgnUAR22iA

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 feature-availability-adjacent-row-changes-iqr-outlier-flags-50ffe870 -d '<json body>'
```

Example prompt: Run adjacent row change detection and IQR outlier flagging on my feature availability data — I have 10 daily rows for dataset group 'prod-features' with populated_features and expected_features counts, and I want to analyze the populated_features field.

## When to prefer this

Choose this endpoint when you have time-series or sequentially ordered feature availability records and need both temporal change detection (adjacent row deltas) and statistical outlier identification (IQR method) in a single call. It is ideal for data quality monitoring pipelines, ML feature store audits, and dataset completeness checks where you want two complementary analytical perspectives for one low flat fee of $0.01 USDC without any external data lookups or predictions.

## Known failure modes

- Fewer than 1 row submitted — request rejected as minItems is 1
- More than 200 rows submitted — request rejected as maxItems is 200
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) — validation failure
- Non-numeric values in populated_features or expected_features — type validation error
- Field selector not in enum ['populated_features','expected_features'] — validation error
- Insufficient data spread for meaningful IQR (e.g. all identical values) — may produce trivial or empty outlier flags

## How this service works

Feature availability — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied feature availability records (populated_features, expected_features). 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 for the submitted rows: (1) an adjacent row changes report showing delta values between consecutive rows for the selected field, and (2) an IQR outlier flags report identifying rows whose field values fall outside the interquartile range bounds. Both reports reference rows by row_id and date, enabling precise identification of anomalous or shifted records.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-adjacent-row-changes-iqr-outlier-flags-50ffe870/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)