# Data Reconciliation — IQR Outlier Flags + Secondary-Measure Weighted Mean

> Data Reconciliation — IQR Outlier Flags + Secondary-Measure Weighted Mean 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).

Analyzes up to 200 data reconciliation records (source_total vs target_total) using IQR-based outlier detection and secondary-measure weighted mean calculations, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-177?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-iqr-outlier-flags-secondary-measure-weighted-mean-ab327acb
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_4rq-OKpX2EnbrWBKzH1Hf

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-iqr-outlier-flags-secondary-measure-weighted-mean-ab327acb -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and a secondary-measure weighted mean on my reconciliation data — I have rows with row_id, date, dataset_group, source_total, and target_total values (up to 50 rows), and I want to flag outliers on the source_total field?

## When to prefer this

Choose this endpoint when you have up to 200 reconciliation records with source and target totals and need fast, local statistical analysis — specifically IQR-based outlier flagging and weighted mean computation — without requiring live lookups, predictions, or external data. Ideal for data quality checks, ETL validation, and audit preparation where deterministic statistical methods are sufficient and privacy or offline computation is preferred.

## Known failure modes

- Submitting more than 200 rows returns an error — batch must be split
- Missing required fields (row_id, dataset_group, date, source_total, target_total) cause validation failure
- Dates not matching YYYY-MM-DD pattern are rejected
- Numeric values outside ±1,000,000,000,000 are rejected
- Fewer than 1 row submitted returns an error
- IQR computation may be unreliable with very small datasets (fewer than 4–5 rows)

## How this service works

Data reconciliation — IQR outlier flags + Secondary-measure weighted mean: IQR outlier flags + Secondary-measure weighted mean 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 for the submitted reconciliation rows: (1) IQR-based outlier flags identifying which rows have source_total or target_total values outside the interquartile range, and (2) a secondary-measure weighted mean calculation across the dataset. Both reports are computed locally using shared analytical engines 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
  },
  "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-iqr-outlier-flags-secondary-measure-weighted-mean-ab327acb/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)