# Dataset Partition Sizes — Record Quality Audit, String Normalisation & IQR Outlier Flags

> Dataset Partition Sizes — Record Quality Audit, String Normalisation & 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 2026-10-19).

Audits record quality, normalises strings, and flags IQR-based outliers for dataset partition size records (row_count, file_mb) — delivering 3 analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-07-203?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/dataset-partition-sizes-record-quality-audit-string-normalisation-iqr-a04f48be
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_3iALtnKAjd_A3jn68JVAe

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 dataset-partition-sizes-record-quality-audit-string-normalisation-iqr-a04f48be -d '<json body>'
```

Example prompt: Can you run a quality audit, string normalisation, and IQR outlier check on my dataset partition records? I have 4 rows with row_id, dataset_group, date, row_count, and file_mb values — I want to analyse the row_count field specifically.

## When to prefer this

Choose this endpoint when you need a combined data quality pass — record auditing, text normalisation, and statistical outlier detection — on dataset partition metadata (row counts and file sizes) in a single $0.01 call. It is purely analytical with no side effects, making it safe to call repeatedly. Prefer it over general-purpose data quality tools when your records specifically contain row_count and file_mb fields and you want IQR-based outlier flagging alongside string cleanup in one bundled response.

## Known failure modes

- Input array is empty or exceeds 200 rows — rejected with validation error
- Required fields (row_id, date, row_count, file_mb, dataset_group) missing from row objects — schema validation failure
- Date fields not matching YYYY-MM-DD pattern — rejected
- Numeric fields outside allowed range — rejected
- Payment of 0.01 USDC not completed — 402 Payment Required response

## How this service works

Dataset partition sizes — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied dataset partition sizes records (row_count, file_mb). 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

Three independent analytical reports covering: (1) a record quality audit identifying missing, malformed, or invalid fields; (2) string normalisation findings for text fields like dataset_group; and (3) IQR-based outlier flags on the chosen numeric field (row_count or file_mb). All reports are computed 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",
     "row_count",
     "file_mb"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "file_mb": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "row_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "file_mb": 1,
     "row_count": 10,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "file_mb": 2,
     "row_count": 20,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "file_mb": 3,
     "row_count": 15,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "file_mb": 4,
     "row_count": 30,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "row_count",
    "file_mb"
   ],
   "type": "string",
   "default": "row_count"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-partition-sizes-record-quality-audit-string-normalisation-iqr-a04f48be/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)