# Dataset Partition Sizes — Record Quality Audit + Exact Row Deduplication + Trailing Mean Series

> Dataset Partition Sizes — Record Quality Audit + Exact Row Deduplication + Trailing Mean Series 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 independent analytical reports (record quality audit, exact row deduplication, and trailing mean series) on dataset partition size records containing row counts and file sizes.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-07-216?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-exact-row-deduplication-5756b3dd
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_H6Wnlr44Owlq3SiTanT_Y

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-exact-row-deduplication-5756b3dd -d '<json body>'
```

Example prompt: Run a record quality audit, exact row deduplication, and trailing mean series on my dataset partition size records — I have 4 rows with row_count and file_mb fields, using a window of 2, and I want the trailing mean computed on row_count.

## When to prefer this

Choose this endpoint when you need three simultaneous analytical operations — quality audit, deduplication, and trailing mean — on dataset partition size data (row_count, file_mb) in a single low-cost call. It is ideal for data engineering pipelines that need lightweight, offline analysis of partition logs without hitting external databases or prediction services. Prefer it over single-report endpoints when you need all three reports together for up to 200 rows at $0.01 USDC.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, row_count, file_mb) on any row — validation error
- Invalid date format (must match YYYY-MM-DD) — schema rejection
- Window size less than 2 or greater than 200 — parameter error
- Field enum value not 'row_count' or 'file_mb' — validation failure
- Empty rows array (minItems: 1 not satisfied) — rejection

## How this service works

Dataset partition sizes — Record quality audit + Exact row deduplication + Trailing mean series: Record quality audit + Exact row deduplication + Trailing mean series 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

Returns three independent reports: (1) a record quality audit identifying missing values, invalid fields, or anomalies across the supplied rows; (2) an exact row deduplication report listing duplicate records found in the input; and (3) a trailing mean series computed over the specified numeric field (row_count or file_mb) using the given sliding window size. 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",
     "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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-partition-sizes-record-quality-audit-exact-row-deduplication-5756b3dd/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)