# Feature Availability Analytics — Record Quality Audit + Date by Category Pivot

> Feature Availability Analytics — Record Quality Audit + Date by Category Pivot 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 date-by-category pivot table, processing up to 200 rows per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-017?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-analytics-record-quality-audit-date-by-category-a8798788
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_g5u37U8c8w14PGlWc2vvm

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-analytics-record-quality-audit-date-by-category-a8798788 -d '<json body>'
```

Example prompt: Can you run a record quality audit and date-by-category pivot on my feature availability data? I have rows with populated_features and expected_features across groups like 'group-a' and 'group-b' dated from 2026-10-01 to 2026-10-04 — I want to see completeness issues and a breakdown by date and group.

## When to prefer this

Choose this endpoint when you need dual analytical reports (quality audit + pivot) on feature availability data in a single low-cost call ($0.01 USDC) without needing live data lookups, predictions, or external integrations. Ideal for data engineers and ML teams who want fast, local computation on batches of up to 200 feature availability records.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) cause row-level rejection
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Null values in both populated_features and expected_features may yield incomplete audit results
- Empty rows array returns an error due to minItems:1 constraint

## How this service works

Feature availability — Record quality audit + Date by category pivot: Record quality audit + Date by category pivot 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

Two independent analytical reports: (1) a record quality audit flagging completeness and consistency issues at the row level based on populated_features vs expected_features, and (2) a date-by-category pivot table aggregating feature counts across dataset_group and date dimensions. Both reports are computed locally from 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-analytics-record-quality-audit-date-by-category-a8798788/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)