# Data Reconciliation — Base-100 Series + IQR Outlier Flags

> Data Reconciliation — Base-100 Series + 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 2026-10-23).

Computes a base-100 indexed time series and IQR-based outlier flags for up to 200 source/target reconciliation rows, returning two analytical reports in one call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-164?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-reconciliation-base-100-series-iqr-outlier-flags-05ecf9ee
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_GlU6hsjDVpLNxTHcN-XIT

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-base-100-series-iqr-outlier-flags-05ecf9ee -d '<json body>'
```

Example prompt: I have 4 rows of reconciliation data — row-1 through row-4 across group-a and group-b from 2026-10-01 to 2026-10-04 with source_totals of 10, 20, 15, 30 and target_totals of 1, 2, 3, 4 — can you run the base-100 series and IQR outlier detection on the source_total field, using row-1 (index 0) as the base?

## When to prefer this

Choose this endpoint when you need both a normalized base-100 time series and IQR outlier detection on reconciliation data in a single low-cost call ($0.01 USDC for both reports). It is ideal for batch analytical tasks on up to 200 pre-aggregated source/target rows where no live data lookup, prediction, or external enrichment is needed — just local statistical computation on data you already have.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, dataset_group, source_total, target_total) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- base_index out of range for the number of rows supplied — error response
- All null values in selected field — unable to compute meaningful index or IQR
- Malformed JSON payload — HTTP 400 or parse error

## How this service works

Data reconciliation — Base-100 series + IQR outlier flags: Base-100 series + 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 analytical reports: (1) a base-100 normalized index series for the chosen field (source_total or target_total) relative to the specified base row, and (2) IQR-based outlier flags for each row indicating which records fall outside the interquartile range — all computed locally from the supplied rows 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-base-100-series-iqr-outlier-flags-05ecf9ee/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)