# Sampling Observations Analytics: Category Frequencies + Adjacent Row Changes

> Sampling Observations Analytics: Category Frequencies + Adjacent Row Changes 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 supplied sampling observation rows to produce two reports: category frequency distributions and adjacent row change metrics, using local analytical engines with no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-104?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/sampling-observations-analytics-category-frequencies-adjacent-row-dcec494d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_BotgsO7634kkpdb7SG28w

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 sampling-observations-analytics-category-frequencies-adjacent-row-dcec494d -d '<json body>'
```

Example prompt: I have a list of up to 200 sampling observation rows with row IDs, dates, group labels, sample values, and sample weights — can you run two analytical reports on them: one showing category frequencies by dataset_group and another showing adjacent row changes in sample_value?

## When to prefer this

Choose this endpoint when you have up to 200 sampling observation records and need two specific analyses — category frequency distribution and adjacent row change detection — in a single low-cost API call ($0.01 USDC). It is ideal for offline/batch statistical summaries of structured sampling data where no live data lookup, prediction, or external enrichment is needed. Prefer it over general-purpose analytics platforms when you want a lightweight, deterministic computation on supplied data only.

## Known failure modes

- Fewer than 1 row submitted — violates minItems constraint
- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) on any row
- Invalid date format not matching YYYY-MM-DD pattern
- Non-numeric or out-of-range values for sample_value or sample_weight
- Invalid enum value for the 'field' parameter (must be sample_value or sample_weight)
- Payment failure via x402 protocol resulting in no response

## How this service works

Sampling observations — Category frequencies + Adjacent row changes: Category frequencies + Adjacent row changes for supplied sampling observations records (sample_value, sample_weight). 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) a category frequency table showing how often each dataset_group value appears across the supplied rows (optionally weighted), and (2) an adjacent row change report showing the difference in the selected field (sample_value or sample_weight) between consecutive rows, based on the submitted data only with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/sampling-observations-analytics-category-frequencies-adjacent-row-dcec494d/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)