# Feature Availability Base-100 Series + IQR Outlier Flags Analytics

> Feature Availability Base-100 Series + IQR Outlier Flags Analytics 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).

Computes Base-100 index series and IQR-based outlier flags for feature availability data (populated_features, expected_features) across up to 200 rows, delivering two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-164?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-base-100-series-iqr-outlier-flags-analytics-f6bb98e6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_9jRf9LuSMCoEQ7Mn3ak0p

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-base-100-series-iqr-outlier-flags-analytics-f6bb98e6 -d '<json body>'
```

Example prompt: I have up to 200 rows of feature availability data with populated_features and expected_features per date and dataset group — can you run a Base-100 series normalization (starting at index 0) on populated_features and flag any IQR outliers across the records?

## When to prefer this

Choose this endpoint when you need both Base-100 time-series normalization and IQR outlier detection on feature availability data in a single call at minimal cost ($0.01 USDC for two reports). Ideal for data quality pipelines auditing feature completeness across dataset groups over time, especially when you want statistical anomaly detection without external model inference or live data lookup. Prefer over single-report endpoints when both normalization and outlier flagging are needed simultaneously.

## Known failure modes

- Input array exceeds 200 rows — rejected with validation error
- Missing required fields (row_id, date, populated_features, expected_features) cause row-level rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- base_index references a row index out of bounds for the supplied array
- Null values in the selected field may affect IQR computation or Base-100 indexing
- All values identical produces degenerate IQR result with no outliers flagged

## How this service works

Feature availability — Base-100 series + IQR outlier flags: Base-100 series + 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 Base-100 series mapping each row's selected field value relative to the chosen base index row, and (2) IQR-based outlier flags indicating which rows fall outside the interquartile range for the selected field — all computed locally without 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-base-100-series-iqr-outlier-flags-analytics-f6bb98e6/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)