# Data Ingestion Batches — Record Quality Audit + String Normalisation + Numeric Interval Filter

> Data Ingestion Batches — Record Quality Audit + String Normalisation + Numeric Interval Filter is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-21).

Runs three analytical reports on data ingestion batch records: a record quality audit, string normalisation check, and numeric interval filter for received_rows or accepted_rows values, for up to 200 rows per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-197?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-21
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-ingestion-batches-record-quality-audit-string-normalisation-b5ebd9f5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7QaNwI2mj2I0fCtwnJoob

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-string-normalisation-b5ebd9f5 -d '<json body>'
```

Example prompt: Can you audit the quality of my ingestion batch records, normalise the dataset_group strings, and filter out any rows where received_rows falls outside 0 to 5000? Here are my rows: row-1 group-a 2025-01-10 received=4500 accepted=4200, row-2 group-b 2025-01-11 received=6000 accepted=5800, row-3 group-a 2025-01-12 received=300 accepted=290.

## When to prefer this

Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, and numeric interval filter) on data ingestion batch records in a single cheap call ($0.01 USDC for up to 200 rows). Ideal for ETL pipeline validation, pre-load data quality checks, and batch anomaly detection on received_rows/accepted_rows metrics without requiring any external data sources or live lookups.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) on any row — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- Numeric values outside ±1,000,000,000,000 bounds — rejected by schema
- Invalid field enum value (not 'received_rows' or 'accepted_rows') — error response
- Empty rows array (minItems:1 violated) — request rejected

## How this service works

Data ingestion batches — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter for supplied data ingestion batches records (received_rows, accepted_rows). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Returns three independent analytical reports for the submitted rows: (1) a record quality audit identifying missing, malformed, or suspect values; (2) a string normalisation report on dataset_group and date string fields; and (3) a numeric interval filter report showing which rows have the selected field (received_rows or accepted_rows) within the specified minimum–maximum range. All analysis is performed locally with no external lookups or predictions.

## 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"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-ingestion-batches-record-quality-audit-string-normalisation-b5ebd9f5/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)