# Data Reconciliation Record Quality Audit and IQR Outlier Flags

> Data Reconciliation Record Quality Audit and 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).

Performs a record quality audit and IQR-based outlier detection on up to 200 source/target reconciliation row pairs, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-014?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-reconciliation-record-quality-audit-and-iqr-outlier-flags-63f98e5b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_PmQKlulB5WSmjPa8XVpa7

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-record-quality-audit-and-iqr-outlier-flags-63f98e5b -d '<json body>'
```

Example prompt: Can you run a record quality audit and IQR outlier check on my reconciliation data? I have rows with row_id, dataset_group, date, source_total, and target_total fields — here are my 10 rows, and I want to flag outliers on the source_total field.

## When to prefer this

Choose this endpoint when you need fast, local statistical quality assessment of reconciliation data — specifically IQR-based outlier detection combined with a record quality audit — without requiring external data lookups, ML predictions, or professional advisory output. Ideal for ETL validation, financial reconciliation checks, and data migration QA on batches up to 200 rows at very low cost ($0.01 per call for two reports).

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, source_total, target_total, dataset_group) cause request rejection
- Invalid date format (not YYYY-MM-DD) triggers a schema validation error
- Values outside the allowed numeric range (±1,000,000,000,000) are rejected
- Empty rows array returns an error
- Invalid field enum value (not 'source_total' or 'target_total') causes rejection

## How this service works

Data reconciliation — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied data reconciliation records (source_total, target_total). 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 identifying issues such as nulls, format errors, or anomalous records across the supplied rows, and (2) IQR-based outlier flags marking which rows contain statistically unusual values in the selected field (source_total or target_total), all computed locally without 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
  },
  "field": {
   "enum": [
    "source_total",
    "target_total"
   ],
   "type": "string",
   "default": "source_total"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-record-quality-audit-and-iqr-outlier-flags-63f98e5b/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)