# Feature Availability Analytics: Adjacent Row Changes + Date-by-Category Pivot

> Feature Availability Analytics: Adjacent Row Changes + Date-by-Category Pivot 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).

Runs two analytical reports on feature availability records — adjacent row change detection and a date-by-category pivot — for up to 200 rows of populated vs. expected feature data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-160?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-date-by-category-e8c27e1e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_loRzxXYG9EnaSG74vhK0P

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-date-by-category-e8c27e1e -d '<json body>'
```

Example prompt: Can you run the adjacent row changes and date-by-category pivot reports on my feature availability data? I have rows with populated_features and expected_features for dataset groups 'group-a' and 'group-b' across dates 2026-10-01 through 2026-10-04 — use populated_features as the target field.

## When to prefer this

Choose this endpoint when you need two specific analytical transformations — adjacent row change detection and date-by-category pivoting — applied simultaneously to feature availability records (populated vs. expected features) in a single low-cost call. It is best suited for data quality monitoring, ETL pipeline auditing, and feature store completeness tracking where you already have the data and need structured analytical summaries without any live lookup or external data enrichment.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, dataset_group, populated_features, expected_features) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- field enum value not one of 'populated_features' or 'expected_features' — rejected
- Null values in numeric fields may affect change calculations — partial results or NaN deltas
- Payment of 0.01 USDC not completed — 402 Payment Required response

## How this service works

Feature availability — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot 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: (1) an adjacent row changes report showing delta values between consecutive rows for the selected field (populated_features or expected_features), and (2) a date-by-category pivot table summarizing the selected field broken down by dataset_group across dates — both 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",
     "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-date-by-category-e8c27e1e/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)