# Record Routing Quality Audit and Missing Value Fill

> Record Routing Quality Audit and 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 2026-10-15).

Audits data quality and fills missing values for accepted_records and rejected_records fields in up to 200 record routing rows, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-19-019?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/record-routing-quality-audit-and-missing-value-fill-8225222d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ls4GlyharMv4YK8RACENv

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 record-routing-quality-audit-and-missing-value-fill-8225222d -d '<json body>'
```

Example prompt: Can you run a quality audit and fill any missing values using median imputation on my record routing data — I have about 50 rows with accepted_records and rejected_records grouped by dataset group and date, and I need both reports?

## When to prefer this

Choose this endpoint when you need a combined data quality audit and missing-value imputation on record routing metrics (accepted_records, rejected_records) for a batch of up to 200 rows. It is best suited for offline or batch data pipeline validation tasks where no live external lookup or prediction is needed, and when you want two structured reports from a single low-cost API call. Prefer this over general-purpose data tools when your data is already structured with row_id, dataset_group, and date fields matching this schema.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row-level validation failure
- Missing required fields (row_id, dataset_group, date) causes row rejection
- Values outside allowed numeric range cause schema validation errors
- Empty rows array returns an error
- Invalid method or field enum value causes parameter rejection

## How this service works

Record routing — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill for supplied record routing records (accepted_records, rejected_records). 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

Two independent analytical reports: (1) a data quality audit flagging issues such as nulls, out-of-range values, and anomalies in the accepted_records and rejected_records fields; (2) an explicit missing-value fill report showing imputed values for the chosen field using the specified method (zero, mean, or median). No external lookups or predictions are made — all analysis uses local analytical engines on the supplied data.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "accepted_records",
     "rejected_records"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "accepted_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "rejected_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "accepted_records": 10,
     "rejected_records": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "accepted_records": 20,
     "rejected_records": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "accepted_records": 15,
     "rejected_records": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "accepted_records": 30,
     "rejected_records": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "accepted_records",
    "rejected_records"
   ],
   "type": "string",
   "default": "accepted_records"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/record-routing-quality-audit-and-missing-value-fill-8225222d/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)