# Data Reconciliation — Daily Sum Series with Missing-Value Fill

> Data Reconciliation — Daily Sum Series with 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 daily sum series data between source and target totals, computing daily aggregates and filling missing values using a chosen fill method (zero, mean, or median) for up to 200 rows across two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-145?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-daily-sum-series-with-missing-value-fill-6ec0f625
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_urGUnPGaUqj9puxKMZXQI

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-daily-sum-series-with-missing-value-fill-6ec0f625 -d '<json body>'
```

Example prompt: I have 150 rows of daily source and target sales totals for October grouped by region — can you reconcile them, fill any missing source_total values using the median, and give me the daily sum series report?

## When to prefer this

Choose this endpoint when you have up to 200 rows of daily time-series records with source and target totals and need both a daily sum aggregation and explicit missing-value imputation in a single call. It is ideal for ETL validation, data pipeline reconciliation, or financial close audits where you need to compare two data feeds by date and dataset group without any external data lookups. Prefer it over general analytics tools when you need the specific combination of daily sum series plus missing-value fill as paired reports at low per-call cost.

## Known failure modes

- Exceeds 200 row limit — request rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) causes schema rejection
- Values outside ±1,000,000,000,000 range rejected by schema
- Invalid method enum value (not zero/mean/median) causes error
- Invalid field enum value (not source_total/target_total) causes error
- Empty rows array (minItems:1 not met) causes rejection

## How this service works

Data reconciliation — Daily sum series + Explicit missing-value fill: Daily sum 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 daily sum series showing aggregated source_total and target_total by date and dataset group, and an explicit missing-value fill report showing which rows had nulls and what values were substituted using the chosen method (zero, mean, or median). Covers up to 200 input rows with no external lookups or predictions.

## 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-daily-sum-series-with-missing-value-fill-6ec0f625/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)