# Feature Availability Analytics: Category Frequencies + Date-by-Category Pivot

> Feature Availability Analytics: Category Frequencies + 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 2026-10-20).

Computes two local analytical reports — category frequency distribution and date-by-category pivot table — for supplied feature availability records (populated_features, expected_features).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-110?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-analytics-category-frequencies-date-by-category-11142a40
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ftw98gh1UsOdEQ1DoXNmh

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-date-by-category-11142a40 -d '<json body>'
```

Example prompt: I have up to 200 feature availability records with fields like row_id, dataset_group, date, populated_features, and expected_features — can you run two reports on them: a category frequency breakdown and a date-by-category pivot table for the populated_features field?

## When to prefer this

Choose this endpoint when you have raw feature availability records (up to 200 rows) and need two quick local analytical outputs — category frequency counts and a date-by-category pivot — without requiring external data lookups or prediction. Ideal for lightweight data quality checks, feature completeness audits, or rollout tracking across dataset groups at a low flat cost of $0.01 USDC for both reports together.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) on any row causes validation error
- Date values not matching YYYY-MM-DD pattern will fail schema validation
- Null values in numeric fields are accepted but may affect pivot aggregation results
- Invalid field enum value (not 'populated_features' or 'expected_features') causes rejection
- Empty rows array (minItems: 1 violated) returns error

## How this service works

Feature availability — Category frequencies + Date by category pivot: Category frequencies + 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 analytical reports: (1) a category frequency table showing how often each dataset_group appears in the supplied records, and (2) a date-by-category pivot table showing the selected field (populated_features or expected_features) values broken down by date and dataset_group. Both reports are computed locally with no external lookups, covering up to 200 input 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-analytics-category-frequencies-date-by-category-11142a40/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)