# Promotion Observations IQR Outlier Flags and Date-by-Category Pivot

> Promotion Observations IQR Outlier Flags and 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 2026-10-22).

Runs two analytical reports on supplied promotion data: IQR-based outlier detection and a date-by-category pivot table for discount_amount or units_sold across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-retail-05-178?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/promotion-observations-iqr-outlier-flags-and-date-by-category-pivot-b1692c81
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_-2E9COvdnwN4UMFLbFCjI

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 promotion-observations-iqr-outlier-flags-and-date-by-category-pivot-b1692c81 -d '<json body>'
```

Example prompt: Can you flag any IQR outliers and generate a date-by-store-group pivot for these 50 promotion records, using units_sold as the analysis field? I have row IDs, store groups, dates, discount amounts, and units sold for each row.

## When to prefer this

Choose this endpoint when you have up to 200 promotion observation records and need both outlier detection and a pivot summary in a single $0.01 call, without requiring any external data sources, live lookups, or predictions. It is ideal for retail analytics workflows where you already have the raw promotion data and want rapid statistical flagging and category-date summarization bundled together.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, store_group, date, discount_amount, units_sold) on any row causes validation error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Insufficient data variance for meaningful IQR computation (e.g. all identical values)
- Invalid field enum value (must be 'discount_amount' or 'units_sold')

## How this service works

Promotion observations — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot for supplied promotion observations records (discount_amount, units_sold). 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 independent analytical reports: (1) IQR outlier flags identifying which rows have statistically extreme values for the selected field (discount_amount or units_sold), including IQR boundaries and flag status per row; (2) a date-by-store-group pivot table summarizing the selected field's values across all supplied promotion records. Both reports are computed locally on 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",
     "discount_amount",
     "units_sold"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "units_sold": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "store_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "discount_amount": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "units_sold": 1,
     "store_group": "group-a",
     "discount_amount": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "units_sold": 2,
     "store_group": "group-b",
     "discount_amount": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "units_sold": 3,
     "store_group": "group-a",
     "discount_amount": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "units_sold": 4,
     "store_group": "group-b",
     "discount_amount": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "discount_amount",
    "units_sold"
   ],
   "type": "string",
   "default": "discount_amount"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/promotion-observations-iqr-outlier-flags-and-date-by-category-pivot-b1692c81/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)