# Feature Availability Analytics — Grouped Numeric Summary + Trailing Mean Series

> Feature Availability Analytics — Grouped Numeric Summary + Trailing Mean Series 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 a grouped numeric summary and a trailing mean time series for feature availability records (populated_features, expected_features) from up to 200 supplied rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-090?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-grouped-numeric-summary-trailing-mean-510212c9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_WFHtij2dE3--frcV0Yo6A

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-grouped-numeric-summary-trailing-mean-510212c9 -d '<json body>'
```

Example prompt: I have feature availability records for two groups — 'group-a' and 'group-b' — across October 2026, with populated_features and expected_features counts per row. Can you give me a grouped numeric summary and a trailing mean series using a window of 3, analyzing the populated_features field?

## When to prefer this

Choose this endpoint when you have up to 200 feature availability records with populated_features and expected_features values and need both a group-level numeric summary and a trailing mean time series in a single low-cost call ($0.01 USDC). It is ideal for data quality monitoring, ML feature store audits, and pipeline health checks where you supply your own data and need offline analytical computation without any external data lookups or predictions.

## Known failure modes

- Fewer than 1 row supplied — minimum item count not met
- More than 200 rows supplied — exceeds maxItems limit
- Invalid date format (must match YYYY-MM-DD pattern)
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features)
- Window value outside allowed range of 2–200
- Field value not one of the allowed enum values: populated_features, expected_features
- Null numeric values in all rows may produce degenerate statistics

## How this service works

Feature availability — Grouped numeric summary + Trailing mean series: Grouped numeric summary + Trailing mean series 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 sharing the same input data: (1) a grouped numeric summary breaking down populated_features and/or expected_features statistics by dataset_group, and (2) a trailing mean time series over the specified window size for the chosen field — all computed locally from the 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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-grouped-numeric-summary-trailing-mean-510212c9/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)