# Data Reconciliation: Category Frequencies + IQR Outlier Flags

> Data Reconciliation: Category Frequencies + 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).

Analyzes up to 200 data reconciliation rows (source_total vs target_total) to produce category frequency distributions and IQR-based outlier flags across dataset groups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-107?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-category-frequencies-iqr-outlier-flags-f22445bf
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_QuyU7uUfYUBs9N5Lv0npo

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-category-frequencies-iqr-outlier-flags-f22445bf -d '<json body>'
```

Example prompt: Can you run category frequency analysis and IQR outlier detection on my reconciliation data? I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and source_totals of 10, 20, 15, 30 paired with target_totals of 1, 2, 3, 4 — analyze the source_total field.

## When to prefer this

Choose this endpoint when you need fast, local statistical analysis of reconciliation data — specifically IQR-based outlier detection and category frequency counting — on batches up to 200 rows without any external data lookups or live predictions. It is ideal for data quality pipelines, ETL validation, and audit workflows where you want to compare source vs target totals across named groups and flag statistical anomalies cheaply at $0.01 per call covering both reports simultaneously.

## Known failure modes

- rows array empty or missing — validation error returned
- rows exceeding 200 items — request rejected
- row missing required fields (row_id, dataset_group, date, source_total, target_total) — schema validation error
- date field 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') — error response
- payment of 0.01 USDC not provided — x402 payment required response

## How this service works

Data reconciliation — Category frequencies + IQR outlier flags: Category frequencies + 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) category frequency counts for the dataset_group field showing how often each group appears in the submitted rows, and (2) IQR-based outlier flags for each row on the selected field (source_total or target_total), identifying which rows fall outside the interquartile range bounds. All analysis is performed locally on the submitted 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
  },
  "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-category-frequencies-iqr-outlier-flags-f22445bf/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)