# Data Reconciliation — Base-100 Series + Explicit Missing-Value Fill

> Data Reconciliation — Base-100 Series + 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 2026-10-19).

Reconciles source and target totals across up to 200 rows by computing a Base-100 indexed series and filling missing values using a chosen strategy (zero, mean, or median).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-169?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-base-100-series-explicit-missing-value-fill-3eb7e657
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_dEgkQTt5LDHJu8E2VzWYt

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-explicit-missing-value-fill-3eb7e657 -d '<json body>'
```

Example prompt: Can you reconcile my source vs target sales data for October 2026 across groups A and B — 4 rows total — index everything to the first row as the base-100 starting point, and fill any missing source_total values using median imputation?

## When to prefer this

Choose this endpoint when you need to both normalize numeric reconciliation data to a Base-100 indexed series AND handle missing values in a single API call, without relying on external data sources or predictions. It is ideal for ETL validation, financial close reconciliation, and audit workflows where you have up to 200 rows of source/target pairs and need two distinct analytical outputs cheaply ($0.01 USDC). Prefer alternatives if you need outlier detection, deduplication, or more than 200 rows.

## Known failure modes

- More than 200 rows submitted — request exceeds maxItems limit
- base_index references a row index out of range for the submitted dataset
- Invalid date format (must be YYYY-MM-DD) causes row validation failure
- source_total or target_total values outside ±1,000,000,000,000 are rejected
- Empty rows array returns validation error
- Missing required fields (row_id, dataset_group, date) cause schema rejection

## How this service works

Data reconciliation — Base-100 series + Explicit missing-value fill: Base-100 series + 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

Returns two analytical reports per call: a Base-100 indexed series showing how each row's values relate to the chosen base row, and a missing-value filled series where nulls in the selected field are replaced using the chosen method (zero, mean, or median). Both reports cover all submitted rows (up to 200) and operate entirely on the supplied 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  },
  "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-explicit-missing-value-fill-3eb7e657/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)