# Data Freshness IQR Outlier Flags + Date-by-Category Pivot

> Data Freshness IQR Outlier Flags + 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 local analytical reports on data freshness records: IQR-based outlier detection and a date-by-category pivot table, for up to 200 rows of age_minutes or record_count data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-178?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/data-freshness-iqr-outlier-flags-date-by-category-pivot-230d3d54
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_966iiOcMzOIpyn7k5dcnO

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 data-freshness-iqr-outlier-flags-date-by-category-pivot-230d3d54 -d '<json body>'
```

Example prompt: Can you flag IQR outliers and generate a date-by-category pivot for my data freshness records? I have rows with row_id, dataset_group, date, age_minutes, and record_count — I want to analyze the age_minutes field across up to 200 rows.

## When to prefer this

Choose this endpoint when you need fast, local statistical analysis of data freshness metrics — specifically IQR-based outlier detection and date-category pivoting — without requiring any external data source, live lookup, or prediction. Ideal for ETL pipeline audits, data quality monitoring dashboards, or any scenario where you have up to 200 rows of age_minutes/record_count records and need two complementary analytical views in a single low-cost call ($0.01 USDC).

## Known failure modes

- Fewer than 1 row submitted — API rejects with validation error
- More than 200 rows submitted — request rejected at maxItems limit
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — row-level validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- Field selector not one of 'age_minutes' or 'record_count' — enum validation error
- All values null for selected field — IQR computation may return undefined outlier bounds

## How this service works

Data freshness — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot for supplied data freshness records (age_minutes, record_count). 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) IQR outlier flags indicating which rows have age_minutes or record_count values outside the interquartile range fence, and (2) a pivot table grouping values by date and dataset_group category — all computed locally without any external lookup or live data access.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-freshness-iqr-outlier-flags-date-by-category-pivot-230d3d54/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)