# Data Ingestion Batches — Record Quality Audit & Adjacent Row Changes

> Data Ingestion Batches — Record Quality Audit & Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).

Performs a two-report analysis on up to 200 data ingestion batch records: a record quality audit and adjacent row change detection for received_rows or accepted_rows fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-011?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-ingestion-batches-record-quality-audit-adjacent-row-changes-cd0b4abc
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_B2Pc_fLvdnqnD_9AifUvM

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-adjacent-row-changes-cd0b4abc -d '<json body>'
```

Example prompt: Can you run a quality audit and adjacent row change analysis on my ingestion batch data? Here are 4 records across two dataset groups — group-a and group-b — from 2026-10-01 to 2026-10-04, with received_rows and accepted_rows values, and I want to analyze the accepted_rows field.

## When to prefer this

Choose this endpoint when you have tabular ingestion batch data with received_rows and accepted_rows metrics and need both a data quality audit and a period-over-period row change analysis in a single call. It is best suited for small-to-medium batch sizes (up to 200 rows) where you want two complementary analytical perspectives — quality flags and trend changes — without any external lookups or live data fetching. Prefer this over generic analytics tools when your data is already structured with dataset_group and date fields and you need fast, local computation results for under $0.01.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Null values in the target field may affect adjacency change calculations
- row_id exceeds 80-character limit — validation error
- Empty rows array — minItems constraint violation

## How this service works

Data ingestion batches — Record quality audit + Adjacent row changes: Record quality audit + Adjacent row changes 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: (1) a record quality audit flagging issues or anomalies in the supplied ingestion batch records, and (2) an adjacent row change report showing differences and eligible percentage changes between consecutive rows — both computed for the specified field (received_rows or accepted_rows) across up to 200 records.

## 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-adjacent-row-changes-cd0b4abc/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)