# Feature Availability Analytics: Daily Sum Series + Date by Category Pivot

> Feature Availability Analytics: Daily Sum Series + 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 analytical reports — a daily sum time series and a date-by-category pivot table — from supplied feature availability records (populated_features and expected_features) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-143?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-daily-sum-series-date-by-category-pivot-63889ebb
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_apIJz5ujw-SSltHEzmSfZ

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-daily-sum-series-date-by-category-pivot-63889ebb -d '<json body>'
```

Example prompt: I have feature availability records with populated_features and expected_features across multiple dataset groups and dates — can you run both a daily sum time series and a date-by-category pivot on the populated_features field for these rows: [{"row_id":"r1","dataset_group":"group-a","date":"2024-01-01","populated_features":80,"expected_features":100},{"row_id":"r2","dataset_group":"group-b","date":"2024-01-01","populated_features":60,"expected_features":90}]?

## When to prefer this

Choose this endpoint when you have up to 200 feature availability records and need both a daily aggregated time series AND a category-by-date pivot in a single low-cost call ($0.01 USDC). Ideal for data quality monitoring, feature engineering audits, or dataset completeness reporting where you supply your own data and need two complementary analytical views without any external data lookups or predictions.

## Known failure modes

- Exceeding the 200-row limit results in rejection or truncation
- Rows missing required fields (row_id, date, populated_features, expected_features) will cause validation errors
- Malformed date strings (not YYYY-MM-DD format) will fail pattern validation
- Numeric values outside ±1,000,000,000,000 range will be rejected
- Empty rows array not accepted (minimum 1 row required)

## How this service works

Feature availability — Daily sum series + Date by category pivot: Daily sum series + 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 derived from the supplied feature availability rows: (1) a daily sum time series showing the aggregated total of the selected field (populated_features or expected_features) for each date, and (2) a date-by-category pivot table cross-tabulating the same field by date and dataset_group, both computed locally on the up-to-200 supplied rows 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-daily-sum-series-date-by-category-pivot-63889ebb/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)