# Brand Mention Inventories — Adjacent Row Changes + Date by Category Pivot

> Brand Mention Inventories — 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 2026-10-16).

Computes adjacent row-over-row changes and a date-by-category pivot table for supplied brand mention inventory records (mention_count, source_count), returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-18-160?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/brand-mention-inventories-adjacent-row-changes-date-by-category-pivot-d281ce0e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_KHhuvgWval1T0jdjQ4lu3

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 brand-mention-inventories-adjacent-row-changes-date-by-category-pivot-d281ce0e -d '<json body>'
```

Example prompt: I have brand mention data for group-a and group-b across October 2026 — can you run both reports on it, the adjacent row changes and the date-by-category pivot, using mention_count as the field? The rows have dates from 2026-10-01 to 2026-10-04 with mention counts 10, 20, 15, 30 and source counts 1, 2, 3, 4.

## When to prefer this

Choose this endpoint when you have an in-hand dataset of brand mention inventory records (up to 200 rows) and need both a row-over-row change analysis and a date-by-category pivot in a single low-cost call. It is ideal for offline/batch analytical workflows where no live data lookup, prediction, or external enrichment is needed — just fast local computation on supplied rows. At $0.01 USDC for two reports, it is highly cost-efficient for structured brand mention time-series analysis.

## Known failure modes

- Missing required row fields (row_id, company_group, date, mention_count, source_count) causes validation error
- Date strings not matching YYYY-MM-DD pattern are rejected
- Exceeding 200 rows returns an error
- Invalid field enum value (not mention_count or source_count) causes rejection
- Empty rows array is not accepted (minItems: 1)
- Payment of 0.01 USDC must be included via x402 protocol or request is rejected

## How this service works

Brand mention inventories — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot for supplied brand mention inventories records (mention_count, source_count). 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 derived entirely from supplied data: (1) an adjacent row changes report showing the difference in the selected metric (mention_count or source_count) between each consecutive row, and (2) a date-by-category pivot table cross-tabulating the metric values by date and company_group. No external data is fetched; all computation is performed on the provided rows (up to 200).

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/brand-mention-inventories-adjacent-row-changes-date-by-category-pivot-d281ce0e/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)