# Feature Availability Analytics — Category Frequencies + IQR Outlier Flags

> Feature Availability Analytics — Category Frequencies + IQR Outlier Flags 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).

Computes category-level frequency distributions and IQR-based outlier flags for up to 200 feature availability records (populated_features or expected_features), returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-107?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-category-frequencies-iqr-outlier-flags-8197823c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_hPhhOEubSOVI3yppEcK7f

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-category-frequencies-iqr-outlier-flags-8197823c -d '<json body>'
```

Example prompt: Can you analyze my feature availability records — I have up to 50 rows each with a row_id, dataset_group, date, populated_features, and expected_features — and give me category frequency counts by group plus flag any IQR outliers in the populated_features column?

## When to prefer this

Choose this endpoint when you need both category frequency analysis and IQR outlier detection on feature availability data in a single paid call. It is well-suited for small-to-medium datasets (up to 200 rows) where you want offline, deterministic statistical analysis without any external API calls, ML predictions, or professional assessments. Prefer it over general analytics services when you specifically have populated_features and expected_features columns and want grouped frequency counts alongside interquartile-range-based anomaly flags in one response.

## 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 (non YYYY-MM-DD) triggers schema rejection
- Null values in the target numeric field may affect IQR computation
- Insufficient data variance (e.g. all identical values) may produce degenerate IQR results
- Payment of 0.01 USDC not included or rejected causes 402 error

## How this service works

Feature availability — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags 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 category frequency table showing how often each dataset_group value appears in the supplied rows, and (2) IQR-based outlier flags identifying which rows have values outside the interquartile range for the selected field (populated_features or expected_features). Results 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-analytics-category-frequencies-iqr-outlier-flags-8197823c/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)