# Data Ingestion Batches — Record Quality Audit & IQR Outlier Flags

> Data Ingestion Batches — Record Quality Audit & 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 2026-10-16).

Performs a two-part quality audit on data ingestion batch records: a record quality check and IQR-based outlier flagging for received_rows or accepted_rows fields across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-014?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-ingestion-batches-record-quality-audit-iqr-outlier-flags-6a1214a1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_vf3mNzUOwSqsm54tjJiYb

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-record-quality-audit-iqr-outlier-flags-6a1214a1 -d '<json body>'
```

Example prompt: Can you run a quality audit and IQR outlier check on my ingestion batch data? I have rows for group-a and group-b from October 2026 with received_rows and accepted_rows counts — flag anything suspicious in the received_rows field.

## When to prefer this

Choose this endpoint when you need a low-cost, self-contained statistical quality check on data ingestion batch records specifically tracking received_rows and accepted_rows metrics. It is ideal when you want both a structural quality audit and IQR outlier detection in a single call without any external data lookups or live predictions. Best suited for ETL monitoring, pipeline validation, and batch data health checks where you have up to 200 rows grouped by dataset and date.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) causes rejection
- Invalid date format (non-YYYY-MM-DD) triggers schema validation failure
- Providing an invalid field enum value (not received_rows or accepted_rows) causes an error
- Empty rows array (minItems: 1 not met) returns an error
- All-null values in the target field may produce degenerate IQR results

## How this service works

Data ingestion batches — Record quality audit + IQR outlier flags: Record quality audit + 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 reports: (1) a record quality audit identifying issues such as null values, format errors, or constraint violations in the submitted rows, and (2) an IQR-based outlier flagging report identifying which rows have statistically anomalous values in the selected field (received_rows or accepted_rows). Both reports are generated 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-record-quality-audit-iqr-outlier-flags-6a1214a1/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)