# Feature Availability Analytics: Adjacent Row Changes + Two-Measure Correlation

> Feature Availability Analytics: Adjacent Row Changes + Two-Measure Correlation 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).

Computes adjacent row change analysis and two-measure correlation for feature availability data (populated_features vs expected_features) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-158?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-analytics-adjacent-row-changes-two-measure-f69295ac
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Q08acbvFsWnsgfiaddpAi

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-analytics-adjacent-row-changes-two-measure-f69295ac -d '<json body>'
```

Example prompt: Can you run adjacent row change analysis and correlation between populated_features and expected_features on this feature availability dataset? I have 4 rows: 2026-10-01 group-a with 10 populated and 1 expected, 2026-10-02 group-b with 20 populated and 2 expected, 2026-10-03 group-a with 15 populated and 3 expected, 2026-10-04 group-b with 30 populated and 4 expected.

## When to prefer this

Choose this endpoint when you have feature availability records with both populated_features and expected_features columns and need both sequential change detection (adjacent row deltas) and correlation analysis in a single cheap call ($0.01 USDC). It is especially useful for data quality monitoring pipelines where you want to catch sudden drops in feature population rates and validate whether actual feature counts track expected counts over time, without needing an external analytics platform.

## Known failure modes

- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Date values not matching YYYY-MM-DD pattern are rejected
- Submitting more than 200 rows exceeds the maxItems limit
- Null values in both measure fields for all rows may produce undefined correlation
- Payment failure (insufficient USDC balance) blocks processing
- Malformed JSON input returns a 400-level error

## How this service works

Feature availability — Adjacent row changes + Two-measure correlation: Adjacent row changes + Two-measure correlation 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 supplied feature availability rows: (1) an adjacent row changes report showing how the selected measure shifts between consecutive rows, and (2) a two-measure correlation report quantifying the statistical relationship between populated_features and expected_features across all rows.

## 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-analytics-adjacent-row-changes-two-measure-f69295ac/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)