# Data Reconciliation IQR Outlier Flags + Explicit Missing-Value Fill

> Data Reconciliation IQR Outlier Flags + Explicit Missing-Value Fill 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).

Detects IQR-based outliers and fills missing values in source/target reconciliation data rows using local analytical engines, returning two reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-180?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-iqr-outlier-flags-explicit-missing-value-fill-89ee6524
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_8lphGDBnuFKxsfl4l9BQK

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-explicit-missing-value-fill-89ee6524 -d '<json body>'
```

Example prompt: I have 50 reconciliation rows with source_total and target_total values — some have nulls. Can you flag IQR outliers and fill the missing source_total values using median, grouped by dataset_group and date?

## When to prefer this

Choose this endpoint when you need both IQR outlier detection and missing-value imputation on reconciliation data (source_total vs target_total pairs) in a single call returning two reports. Ideal for data quality checks on ETL pipelines, financial close processes, or audit workflows with up to 200 rows. Prefer this over separate outlier or imputation tools when both reports are needed together at low cost ($0.01 USDC). Not suitable for prediction, live data lookup, or datasets exceeding 200 rows.

## Known failure modes

- More than 200 rows submitted — request will be rejected
- Missing required fields (row_id, dataset_group, date, source_total, target_total) — validation error
- Date field does not match YYYY-MM-DD format — schema validation failure
- Numeric values outside allowed range (-1T to +1T) — schema rejection
- Invalid enum value for field or method parameters — rejected with schema error
- Empty rows array — rejected due to minItems:1 constraint

## How this service works

Data reconciliation — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill 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 analytical reports covering: (1) IQR-based outlier flags identifying which rows have source_total or target_total values outside the interquartile range, and (2) a filled dataset where missing values in the specified field have been replaced using the chosen method (zero, mean, or median). All analysis is performed locally 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-iqr-outlier-flags-explicit-missing-value-fill-89ee6524/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)