# Feature Availability Record Quality Audit + Grouped Numeric Summary

> Feature Availability Record Quality Audit + Grouped Numeric Summary is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).

Runs two analytical reports on supplied feature availability records: a record quality audit and a grouped numeric summary, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-005?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-record-quality-audit-grouped-numeric-summary-79013aa9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_aVNSWnCpOtiGNWSzP1PZq

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 feature-availability-record-quality-audit-grouped-numeric-summary-79013aa9 -d '<json body>'
```

Example prompt: Can you run a record quality audit and grouped numeric summary on my feature availability data? I have 4 rows: row-1 on 2026-10-01 in group-a with 10 populated and 1 expected, row-2 on 2026-10-02 in group-b with 20 populated and 2 expected, row-3 on 2026-10-03 in group-a with 15 populated and 3 expected, and row-4 on 2026-10-04 in group-b with 30 populated and 4 expected — analyze the populated_features field.

## When to prefer this

Choose this endpoint when you need two simultaneous analytical reports — a quality audit and a grouped summary — on a batch of up to 200 feature availability records without any external data lookups or live predictions. It is especially useful for ML data ops teams checking feature population completeness across dataset groups on a per-batch basis at low cost ($0.01 per call covering both reports).

## Known failure modes

- Submitting more than 200 rows returns an error — batch must be split
- Rows missing required fields (row_id, dataset_group, date, populated_features, expected_features) will fail schema validation
- Dates not matching YYYY-MM-DD pattern will be rejected
- Null values for both numeric fields on a row may affect report completeness
- Invalid field enum value (anything other than 'populated_features' or 'expected_features') will be rejected
- Payment failure (insufficient USDC or x402 not supported) will prevent execution

## How this service works

Feature availability — Record quality audit + Grouped numeric summary: Record quality audit + Grouped numeric summary for supplied feature availability records (populated_features, expected_features). 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: (1) a record quality audit identifying issues in the supplied feature availability rows, and (2) a grouped numeric summary aggregating the selected field (populated_features or expected_features) by dataset_group. Both reports are computed locally from 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",
     "populated_features",
     "expected_features"
    ],
    "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
     },
     "expected_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "populated_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_features": 1,
     "populated_features": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_features": 2,
     "populated_features": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_features": 3,
     "populated_features": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_features": 4,
     "populated_features": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "populated_features",
    "expected_features"
   ],
   "type": "string",
   "default": "populated_features"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-record-quality-audit-grouped-numeric-summary-79013aa9/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)