# Feature Availability Analytics: Adjacent Row Changes + Explicit Missing-Value Fill

> Feature Availability Analytics: Adjacent Row Changes + Explicit Missing-Value Fill 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 to produce two reports: adjacent row change detection and explicit missing-value fill, using local analytical engines on up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-162?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-explicit-missing-0ef3f33e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_8UlFE-DMBT4XSyTNNERHN

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-explicit-missing-0ef3f33e -d '<json body>'
```

Example prompt: I have up to 200 rows of feature availability records with populated_features and expected_features per date and dataset group — can you run the adjacent row changes report and fill any missing populated_features values using median imputation on that data?

## When to prefer this

Choose this endpoint when you need both adjacent row change detection and missing-value imputation on feature availability data in a single call, with no need for external lookups or predictions. It is well-suited for data pipeline audits, feature engineering QA, and time-series completeness checks on small to medium datasets (up to 200 rows). Prefer this over general-purpose analytics when your data conforms to the populated_features/expected_features schema and you want two bundled reports at low cost.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- Unknown fill method value (not zero/mean/median) — enum validation failure
- Unknown field value (not populated_features/expected_features) — enum validation failure
- Empty rows array (minItems:1 violated) — validation error
- row_id missing or empty string — required field error
- Numeric values out of allowed range (±1 trillion) — validation error

## How this service works

Feature availability — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill 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 analytical reports: (1) an adjacent row change report identifying shifts between consecutive rows in the specified field, and (2) a missing-value fill report showing imputed values using the chosen method (zero, mean, or median) — all computed locally on the supplied records with no external lookups.

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-adjacent-row-changes-explicit-missing-0ef3f33e/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)