# Join Coverage Analytics: Adjacent Row Changes + Date-by-Category Pivot

> Join Coverage 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 2026-10-15).

Analyzes join coverage data (matched_rows, source_rows) across up to 200 rows, producing two reports: adjacent row-over-row change analysis and a date-by-category pivot table.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-160?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-analytics-adjacent-row-changes-date-by-category-pivot-8948be07
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_vjPOWSImLfwwD5LHmWBkH

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 join-coverage-analytics-adjacent-row-changes-date-by-category-pivot-8948be07 -d '<json body>'
```

Example prompt: Can you analyze my join coverage data for the last week — I have matched_rows and source_rows across two groups, 'group-a' and 'group-b', for dates 2026-10-01 through 2026-10-04, and I want both the adjacent row-over-row changes and a pivot by date and category for the matched_rows field?

## When to prefer this

Choose this endpoint when you have pre-collected join coverage tabular data (matched_rows and source_rows per date and dataset group) and need two specific analytical views — row-over-row change detection and a date-by-category pivot — without any external data lookup, prediction, or live monitoring. It is ideal for ad-hoc, offline analysis of up to 200 records at a very low cost ($0.01 USDC for both reports). Prefer alternatives if you need more than 200 rows, live data ingestion, or different analytical transformations such as time-series forecasting or anomaly detection.

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, date, dataset_group, matched_rows, source_rows) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Null values in numeric fields may affect adjacent row change calculations
- field enum value not one of 'matched_rows' or 'source_rows' — validation error
- Empty rows array — no analysis possible

## How this service works

Join coverage — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot for supplied join coverage records (matched_rows, source_rows). 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 computed locally on the supplied data: (1) an adjacent row changes report showing how matched_rows or source_rows shift between consecutive records, and (2) a date-by-category pivot table summarizing the chosen metric across dataset groups and dates. No external lookups or predictions are made; all analysis is based solely on the input rows provided (up to 200 rows).

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "matched_rows",
     "source_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "matched_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_rows": 1,
     "matched_rows": 10,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_rows": 2,
     "matched_rows": 20,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_rows": 3,
     "matched_rows": 15,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_rows": 4,
     "matched_rows": 30,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "matched_rows",
    "source_rows"
   ],
   "type": "string",
   "default": "matched_rows"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-analytics-adjacent-row-changes-date-by-category-pivot-8948be07/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)