# Returns Handling Analytics — Numeric Distribution & Adjacent Row Changes

> Returns Handling Analytics — Numeric Distribution & 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 2026-11-02).

Computes numeric distribution statistics and adjacent row change analysis for returns handling records (returned_units, processing_minutes) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-retail-04-077?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/returns-handling-analytics-numeric-distribution-adjacent-row-changes-b6344700
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_XBkJ8kiYD1WvE4nwW64To

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 returns-handling-analytics-numeric-distribution-adjacent-row-changes-b6344700 -d '<json body>'
```

Example prompt: Can you run a numeric distribution report and adjacent row changes analysis on my returns handling data? I have up to 50 rows with returned_units and processing_minutes across store groups, and I want the analysis focused on returned_units.

## When to prefer this

Choose this endpoint when you need both a numeric distribution summary and row-over-row delta analysis on returns handling data (returned_units or processing_minutes) in a single call, and your dataset is 200 rows or fewer. It is a self-contained, offline computation with no live lookups — ideal for quick batch analysis of retail returns records without requiring external data sources or prediction models.

## Known failure modes

- Exceeds 200-row limit — request rejected or truncated
- Missing required fields (row_id, date, returned_units, processing_minutes) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Null values in numeric fields may affect distribution calculations
- Field selector not matching enum values ('returned_units' or 'processing_minutes') — error

## How this service works

Returns handling — Numeric distribution + Adjacent row changes: Numeric distribution + Adjacent row changes for supplied returns handling records (returned_units, processing_minutes). 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

Two analytical reports: (1) a numeric distribution breakdown of the selected field (returned_units or processing_minutes) showing statistical spread across the supplied rows, and (2) an adjacent row changes report showing delta values between consecutive records — all 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",
     "store_group",
     "date",
     "returned_units",
     "processing_minutes"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "store_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "returned_units": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "processing_minutes": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "store_group": "group-a",
     "returned_units": 10,
     "processing_minutes": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "store_group": "group-b",
     "returned_units": 20,
     "processing_minutes": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "store_group": "group-a",
     "returned_units": 15,
     "processing_minutes": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "store_group": "group-b",
     "returned_units": 30,
     "processing_minutes": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "returned_units",
    "processing_minutes"
   ],
   "type": "string",
   "default": "returned_units"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/returns-handling-analytics-numeric-distribution-adjacent-row-changes-b6344700/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)