# Sampling Observations — Adjacent Row Changes + Date-by-Category Pivot

> Sampling Observations — 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).

Runs two local analytical reports on supplied sampling observation rows: an adjacent-row change analysis and a date-by-category pivot table.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-160?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/sampling-observations-adjacent-row-changes-date-by-category-pivot-b7bf7266
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_yzeu1bIRl4bGRa8jBl2tW

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-adjacent-row-changes-date-by-category-pivot-b7bf7266 -d '<json body>'
```

Example prompt: I have up to 200 sampling observation rows with dates, group labels, sample values, and weights — can you run two reports on them: one showing adjacent row-to-row changes in sample_value, and another pivoting sample_value by date and category group?

## When to prefer this

Choose this endpoint when you need both a sequential change analysis and a cross-tabulated date-by-category pivot in one call for up to 200 sampling observation records containing numeric values and group labels. It is ideal for batch offline analysis of structured tabular data where no external data, predictions, or live lookups are needed. Particularly suited for survey data, sensor readings, or quality-control samples organized by group and date.

## Known failure modes

- Fewer than 1 row supplied — validation error
- More than 200 rows supplied — request rejected
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) — schema validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- Numeric values outside ±1,000,000,000,000 range — validation error
- Invalid field enum value (not sample_value or sample_weight) — schema error

## How this service works

Sampling observations — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot 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 analytical reports: (1) an adjacent row changes table showing value deltas between consecutive rows for the selected field, and (2) a date-by-category pivot table summarizing the selected field (sample_value or sample_weight) across dataset groups and dates. Both reports are 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",
     "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-adjacent-row-changes-date-by-category-pivot-b7bf7266/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)