# Data Reconciliation Analytics: Record Quality Audit + Exact Row Deduplication + Category Frequencies

> Data Reconciliation Analytics: Record Quality Audit + Exact Row Deduplication + Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Runs three local analytical reports on reconciliation data: a record quality audit, exact row deduplication, and category frequency analysis — all in a single call for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-212?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-reconciliation-analytics-record-quality-audit-exact-row-38c6935d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_G78RWA0-7jjpo_x5eHxR9

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-reconciliation-analytics-record-quality-audit-exact-row-38c6935d -d '<json body>'
```

Example prompt: Can you audit the quality of my reconciliation records, deduplicate any exact duplicate rows, and give me category frequencies — I have up to 50 rows with row IDs, dates, dataset groups, and source/target totals I'll pass in?

## When to prefer this

Choose this endpoint when you need three analytical operations — quality audit, deduplication, and category frequency analysis — on a single batch of reconciliation data (up to 200 rows) without making three separate API calls. It is ideal when your workflow involves ETL validation, finance reconciliation, or data migration checks where you want to assess data health, remove duplicates, and understand category distributions in one pass. It does not perform live data lookups, ML predictions, or external integrations.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) causes row-level rejection
- Malformed date strings not matching YYYY-MM-DD pattern are flagged or rejected
- source_total or target_total values outside the allowed range (-1e12 to 1e12) fail schema validation
- Empty rows array returns an error
- Additional properties in row objects are rejected due to additionalProperties:false

## How this service works

Data reconciliation — Record quality audit + Exact row deduplication + Category frequencies: Record quality audit + Exact row deduplication + Category frequencies for supplied data reconciliation records (source_total, target_total). 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: (1) a record quality audit flagging missing, null, or malformed fields across the supplied rows; (2) exact row deduplication results identifying and removing fully identical rows; and (3) a category frequency table showing how often each dataset_group value appears across the dataset. All reports are computed 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",
     "source_total",
     "target_total"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_total": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "target_total": {
      "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",
     "source_total": 10,
     "target_total": 1,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_total": 20,
     "target_total": 2,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_total": 15,
     "target_total": 3,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_total": 30,
     "target_total": 4,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-analytics-record-quality-audit-exact-row-38c6935d/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)