# Sampling Observations — Numeric Interval Filter + Adjacent Row Changes

> Sampling Observations — Numeric Interval Filter + 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).

Applies a numeric interval filter and adjacent row change analysis to supplied sampling observation records (sample_value, sample_weight) for up to 200 rows, returning 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-127?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-numeric-interval-filter-adjacent-row-changes-ebd4b83f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_YZlxlUwV1FVhaHpFO_HCP

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-numeric-interval-filter-adjacent-row-changes-ebd4b83f -d '<json body>'
```

Example prompt: I have 150 sampling observation rows with columns row_id, dataset_group, date, sample_value, and sample_weight — can you filter them to only rows where sample_value falls between 5 and 95, and also compute adjacent row changes for sample_value, giving me both reports?

## When to prefer this

Choose this endpoint when you have up to 200 sampling observation records with sample_value and sample_weight fields and need two offline analytical operations — numeric range filtering and adjacent row change detection — in a single low-cost call. Prefer it over general analytics platforms when you want a deterministic, privacy-safe local computation with no external data lookups and a predictable $0.01 flat cost.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected
- Missing required row fields (row_id, dataset_group, date, sample_value, sample_weight) causes validation error
- minimum greater than maximum produces empty or invalid interval filter results
- Non-date-formatted date strings fail schema pattern validation
- Payment of 0.01 USDC not included or declined via x402 protocol blocks execution
- Null sample_value or sample_weight values may affect change computation output

## How this service works

Sampling observations — Numeric interval filter + Adjacent row changes: Numeric interval filter + 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 local analytical reports: (1) rows from the supplied dataset whose target field (sample_value or sample_weight) falls within the specified [minimum, maximum] interval, and (2) adjacent row change analysis showing value differences between consecutive rows — all computed offline on the supplied data, 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"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/sampling-observations-numeric-interval-filter-adjacent-row-changes-ebd4b83f/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)