# Data Reconciliation — Numeric Row Ordering + Explicit Missing-Value Fill

> Data Reconciliation — Numeric Row Ordering + 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).

Sorts reconciliation rows numerically and fills missing source_total or target_total values using zero, mean, or median imputation for up to 200 records, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-124?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-numeric-row-ordering-explicit-missing-value-fill-e8e3ac21
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_96T4V12e8UzWfxitgrKFA

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-numeric-row-ordering-explicit-missing-value-fill-e8e3ac21 -d '<json body>'
```

Example prompt: I have a reconciliation dataset with up to 50 rows — each row has a row_id, dataset_group, date, source_total, and target_total, but some source_total values are missing. Can you sort the rows numerically and fill those missing source_total values using median imputation?

## When to prefer this

Choose this endpoint when you need both numeric ordering and missing-value imputation applied to reconciliation data in a single call, with results delivered as two independent local reports. It is well-suited for financial or accounting reconciliation workflows where source and target totals need cleaning before comparison or audit, and no external data sources or predictions are required.

## Known failure modes

- More than 200 rows submitted — exceeds maximum row limit
- Invalid date format not matching YYYY-MM-DD pattern
- source_total or target_total values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Missing required fields (row_id, dataset_group, date) on any row
- Invalid enum value for field or method parameters
- Empty rows array submitted

## How this service works

Data reconciliation — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + 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 independent analytical reports covering up to 200 reconciliation rows: one with rows sorted in numeric order, and one with missing values in the specified field (source_total or target_total) filled using the chosen method (zero, mean, or median). No external lookups or predictions are made — all computation uses the supplied data only.

## 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-numeric-row-ordering-explicit-missing-value-fill-e8e3ac21/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)