# Data Ingestion Batches — Record Quality Audit, String Normalisation & Date-by-Category Pivot

> Data Ingestion Batches — Record Quality Audit, String Normalisation & 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 2026-10-19).

Runs three analytical reports on up to 200 data ingestion batch rows: a record quality audit, string normalisation, and a date-by-category pivot for received_rows or accepted_rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-206?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-ingestion-batches-record-quality-audit-string-normalisation-date-3ad230bd
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_uh7SwY_XbTTSexhkwhTQk

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-date-3ad230bd -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and date-by-category pivot on these ingestion batch rows — I have about 50 rows with row_id, dataset_group, date, received_rows, and accepted_rows — and use the accepted_rows field for the pivot?

## When to prefer this

Choose this endpoint when you have up to 200 data ingestion batch records and need three simultaneous analyses — quality audit, string normalisation, and date pivot — in a single cheap call ($0.01 USDC). It is ideal for ETL pipeline monitoring, batch acceptance rate review, and data onboarding quality checks where no live external lookup or ML prediction is needed and all computation is local to the supplied data.

## Known failure modes

- More than 200 rows submitted — request rejected, trim to 200 max
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error
- Date field not in YYYY-MM-DD format — pattern validation failure
- Numeric fields outside allowed range (±1,000,000,000,000) — schema rejection
- Empty rows array — minItems:1 constraint failure
- Invalid field enum value (not 'received_rows' or 'accepted_rows') — schema error

## How this service works

Data ingestion batches — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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: (1) a record quality audit flagging completeness and consistency issues across the supplied rows, (2) a string normalisation report standardising dataset_group and other string fields, and (3) a date-by-category pivot table summarising either received_rows or accepted_rows grouped by date and dataset_group. All analysis is performed 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",
     "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-string-normalisation-date-3ad230bd/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)