# Feature Availability — Record Quality Audit + Trailing Mean Series

> Feature Availability — Record Quality Audit + 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 2026-10-16).

Runs two local analytical reports on supplied feature availability data: a record quality audit and a trailing mean series, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-010?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-record-quality-audit-trailing-mean-series-a6f718bc
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_YV3miSOqZEubsFxkaqy_z

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-record-quality-audit-trailing-mean-series-a6f718bc -d '<json body>'
```

Example prompt: Can you run a record quality audit and trailing mean series on my feature availability data? I have 4 rows for group-a and group-b from 2026-10-01 to 2026-10-04, with populated_features values of 10, 20, 15, 30 and expected_features of 1, 2, 3, 4 — use a window of 3 and analyze the populated_features field.

## When to prefer this

Choose this endpoint when you need to assess the quality of feature availability records AND compute rolling averages in a single low-cost call. It is ideal for ML/data engineering workflows where you want to validate feature population completeness and monitor trends over time, without requiring a live data connection or external lookup. Best suited for batch sizes of up to 200 rows where two complementary reports (quality audit + trailing mean) are both needed at once.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Date strings not in YYYY-MM-DD format — pattern validation failure
- Window value outside range 2–200 — rejected
- Null values in numeric fields may affect mean calculation output
- Invalid field enum value — rejected

## How this service works

Feature availability — Record quality audit + Trailing mean series: Record quality audit + 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: (1) a record quality audit highlighting issues, completeness rates, or anomalies in the supplied feature availability rows, and (2) a trailing mean series showing rolling averages of the selected field (populated_features or expected_features) over the specified window — all computed locally without any live lookup or external data.

## 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-record-quality-audit-trailing-mean-series-a6f718bc/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)