# Feature Availability — Record Quality Audit + Daily Sum Series

> Feature Availability — Record Quality Audit + Daily Sum Series 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).

Runs two local analytical reports on feature availability data: a record quality audit and a daily sum series, for up to 200 rows of populated/expected feature counts.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-009?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/feature-availability-record-quality-audit-daily-sum-series-02bee5d4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ly5m2EumMyKs33e_IK2oY

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-daily-sum-series-02bee5d4 -d '<json body>'
```

Example prompt: Can you run a record quality audit and daily sum series on my feature availability data? I have rows with row_id, dataset_group, date, populated_features, and expected_features — up to 50 rows — and I want to analyze populated_features.

## When to prefer this

Choose this endpoint when you need two simultaneous analytical reports — a quality audit and a daily aggregation — on feature availability data with populated/expected feature counts, all processed locally without any external API calls or live data lookups, and when your dataset is 200 rows or fewer. It is especially suited for ML data pipeline monitoring, feature store observability, and lightweight data quality checks at low cost ($0.01 USDC for both reports).

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Invalid date format (must match YYYY-MM-DD pattern) causes row rejection
- Null values in numeric fields may reduce accuracy of sum series
- field enum value not one of 'populated_features' or 'expected_features' causes error

## How this service works

Feature availability — Record quality audit + Daily sum series: Record quality audit + Daily sum series 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 data issues, nulls, or anomalies in the supplied feature availability rows, and (2) a daily sum series aggregating the selected field (populated_features or expected_features) by date — all computed locally without external lookups, covering up to 200 rows.

## 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-daily-sum-series-02bee5d4/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)