# Feature Availability IQR Outlier Flags + Date by Category Pivot Analytics

> Feature Availability IQR Outlier Flags + Date by Category Pivot Analytics is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Analyzes feature availability records to produce IQR-based outlier flags and a date-by-category pivot table for up to 200 rows of populated/expected feature data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-178?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-iqr-outlier-flags-date-by-category-pivot-analytics-bf5f512e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Cw2qsUQ9n1n7ZZ3UnyqzZ

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-iqr-outlier-flags-date-by-category-pivot-analytics-bf5f512e -d '<json body>'
```

Example prompt: Can you run an IQR outlier detection and a date-by-category pivot on my feature availability data? I have rows with populated_features and expected_features for groups like 'group-a' and 'group-b' across dates in October 2026 — analyze the populated_features field.

## When to prefer this

Choose this endpoint when you need both IQR-based outlier detection and a date-by-category pivot on feature availability data (populated_features or expected_features) in a single low-cost call. Ideal for data quality checks, ML pipeline monitoring, and feature completeness audits on batches up to 200 rows. Prefer this over general analytics tools when working specifically with dataset_group + date + feature count records and you want two complementary statistical views at once.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Malformed date strings not matching YYYY-MM-DD pattern cause schema rejection
- Insufficient data variance for meaningful IQR calculation if too few rows or all values are identical
- Null values in the selected numeric field may reduce outlier detection accuracy

## How this service works

Feature availability — IQR outlier flags + Date by category pivot: IQR outlier flags + 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

Returns two independent analytical reports: (1) IQR outlier flags identifying which rows have statistically anomalous values for the selected field (populated_features or expected_features), and (2) a date-by-category pivot table summarizing the selected field values grouped by date and dataset_group. Both reports are computed locally from the supplied rows (up to 200) 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-iqr-outlier-flags-date-by-category-pivot-analytics-bf5f512e/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)