# Null Coverage Analytics — Adjacent Row Changes & IQR Outlier Flags

> Null Coverage Analytics — 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 2026-10-18).

Analyzes supplied null coverage records (missing_values, total_values) to produce two reports: adjacent row change detection and IQR-based outlier flagging, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-03-157?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/null-coverage-analytics-adjacent-row-changes-iqr-outlier-flags-dc484c0c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_3A6PNElBJBHJ5GuJjTcHl

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 null-coverage-analytics-adjacent-row-changes-iqr-outlier-flags-dc484c0c -d '<json body>'
```

Example prompt: I have null coverage records for my datasets — can you run both an adjacent-row change analysis and an IQR outlier check on the missing_values field for these rows: [{"row_id":"r1","dataset_group":"sales","date":"2024-01-01","missing_values":5,"total_values":100},{"row_id":"r2","dataset_group":"sales","date":"2024-01-02","missing_values":80,"total_values":100},{"row_id":"r3","dataset_group":"sales","date":"2024-01-03","missing_values":6,"total_values":100}]?

## When to prefer this

Choose this endpoint when you need both adjacent-row change detection AND IQR outlier flagging on null coverage data in a single call, without requiring any external data source or live lookup. It is ideal for data quality pipelines that batch-process completeness metrics, audit missing value patterns, or need statistical anomaly flags on small-to-medium datasets (up to 200 rows) at very low cost.

## Known failure modes

- Fewer than 1 row supplied — minimum row count not met
- More than 200 rows submitted — exceeds maximum batch size
- Missing required fields (row_id, date, missing_values, total_values) on any row — validation error
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Null or non-numeric values in both missing_values and total_values may reduce report accuracy
- Unknown field enum value (not 'missing_values' or 'total_values') causes request failure

## How this service works

Null coverage — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied null coverage records (missing_values, total_values). 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

Two independent analytical reports per call: (1) adjacent row change report showing how missing_values or total_values shift between consecutive rows, and (2) IQR outlier flags identifying rows whose values fall outside the interquartile range boundary, all computed locally on 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",
     "missing_values",
     "total_values"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "total_values": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "missing_values": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "total_values": 1,
     "dataset_group": "group-a",
     "missing_values": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "total_values": 2,
     "dataset_group": "group-b",
     "missing_values": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "total_values": 3,
     "dataset_group": "group-a",
     "missing_values": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "total_values": 4,
     "dataset_group": "group-b",
     "missing_values": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "missing_values",
    "total_values"
   ],
   "type": "string",
   "default": "missing_values"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/null-coverage-analytics-adjacent-row-changes-iqr-outlier-flags-dc484c0c/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)