# Feature Availability — Record Quality Audit + Exact Row Deduplication

> Feature Availability — Record Quality Audit + Exact Row Deduplication 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 independent analytical reports on supplied feature availability records: a record quality audit and exact row deduplication, for up to 200 rows per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-002?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-exact-row-deduplication-9b428b9e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_re6Bhn7hXQ9BjpSzww5_E

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-exact-row-deduplication-9b428b9e -d '<json body>'
```

Example prompt: Can you run a quality audit and exact row deduplication check on these feature availability records — I have rows for groups 'model-v1' and 'model-v2' across dates in October 2026, each with populated_features and expected_features counts, and I want both reports back for up to 50 rows?

## When to prefer this

Choose this endpoint when you need both a data quality audit and exact deduplication on feature availability records in a single low-cost call. It is well-suited for ML/data engineering pipelines where you want to validate populated vs expected feature counts and catch duplicate rows before downstream ingestion, without any external API calls or live data lookups. Ideal for batches up to 200 rows where local, self-contained analytical reports are sufficient.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Date not matching YYYY-MM-DD format — pattern validation failure
- Empty rows array — minItems constraint violation
- populated_features or expected_features out of allowed numeric range — schema rejection

## How this service works

Feature availability — Record quality audit + Exact row deduplication: Record quality audit + Exact row deduplication 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 covering completeness, consistency, and anomalies in populated_features vs expected_features across rows; and (2) an exact row deduplication report identifying any duplicate rows across the supplied dataset. Both reports are generated using local analytical engines 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-record-quality-audit-exact-row-deduplication-9b428b9e/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)