# Data Ingestion Batches — Category Frequencies + IQR Outlier Flags

> Data Ingestion Batches — Category Frequencies + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).

Analyzes up to 200 data ingestion batch records to produce category frequency distributions and IQR-based outlier flags for received_rows or accepted_rows metrics.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-107?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-ingestion-batches-category-frequencies-iqr-outlier-flags-f41e414d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1wWYT3pBQHw0_676hShBE

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-ingestion-batches-category-frequencies-iqr-outlier-flags-f41e414d -d '<json body>'
```

Example prompt: Analyze my ingestion batch records — I have rows with dataset_group, date, received_rows, and accepted_rows — and give me category frequencies per dataset group plus IQR outlier flags for the received_rows field. Here are up to 200 rows of batch data.

## When to prefer this

Choose this endpoint when you need a quick, offline statistical profile of data ingestion batch records specifically — combining group-level frequency analysis with IQR outlier detection in a single call for up to 200 rows. Prefer it over generic analytics tools when your data conforms to the ingestion batch schema (received_rows, accepted_rows, dataset_group, date) and you want two bundled reports at low cost with no external dependencies.

## Known failure modes

- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — schema validation failure
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- Numeric values out of allowed range (±1,000,000,000,000) — validation error
- All values identical — IQR is zero, no outliers detectable

## How this service works

Data ingestion batches — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags for supplied data ingestion batches records (received_rows, accepted_rows). 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 independent analytical reports for the supplied batch records: (1) category frequency counts showing how often each dataset_group value appears, and (2) IQR-based outlier flags for the selected numeric field (received_rows or accepted_rows), marking rows whose values fall outside the interquartile range bounds. All analysis is performed locally with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-ingestion-batches-category-frequencies-iqr-outlier-flags-f41e414d/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)