# Data Reconciliation — Numeric Distribution + IQR Outlier Flags

> Data Reconciliation — Numeric Distribution + 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).

Computes numeric distribution statistics and IQR-based outlier flags for up to 200 data reconciliation records containing source and target totals.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-080?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-numeric-distribution-iqr-outlier-flags-4188380c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_2SivFEhTYFgkP4lPztxDh

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-numeric-distribution-iqr-outlier-flags-4188380c -d '<json body>'
```

Example prompt: I have 150 reconciliation rows with source_total and target_total values — can you run a numeric distribution analysis and IQR outlier detection on the source_total field and tell me which rows look statistically anomalous?

## When to prefer this

Choose this endpoint when you have tabular reconciliation data with source and target numeric totals and need both distribution statistics and IQR-based outlier detection in a single call without any external data lookup, live market data, or prediction. Ideal for ETL validation, financial reconciliation audits, and data quality checks on datasets up to 200 rows. The flat $0.01 USDC fee covers two reports (distribution + outlier flags) simultaneously, making it cost-efficient for paired analytical needs.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) — schema rejection
- Date strings not matching YYYY-MM-DD pattern — validation error
- Numeric values outside ±1,000,000,000,000 range — schema rejection
- Invalid field enum value (not source_total or target_total) — validation error
- All source_total or target_total values are null — insufficient data for distribution computation

## How this service works

Data reconciliation — Numeric distribution + IQR outlier flags: Numeric distribution + 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

Two independent analytical reports covering: numeric distribution statistics (min, max, mean, median, quartiles, std dev) and IQR-based outlier flags identifying which rows fall outside the interquartile fences, for the selected field across all submitted reconciliation rows (up to 200).

## 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-numeric-distribution-iqr-outlier-flags-4188380c/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)