# Feature Availability Analytics — Record Quality Audit, String Normalisation & Adjacent Row Changes

> Feature Availability Analytics — Record Quality Audit, String Normalisation & Adjacent Row Changes 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).

Runs three independent analytical reports (record quality audit, string normalisation, and adjacent row change detection) on up to 200 feature availability records containing populated_features and expected_features fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-200?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-record-quality-audit-string-3908bf23
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_tY09NDxfXO5hKOONY3xGW

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-record-quality-audit-string-3908bf23 -d '<json body>'
```

Example prompt: Can you run the three-part quality check on my feature availability records — audit record quality, normalise strings, and flag adjacent row changes — for this batch of up to 200 rows with populated_features and expected_features fields across my dataset groups?

## When to prefer this

Choose this endpoint when you need three bundled data quality reports — record quality audit, string normalisation, and adjacent row change detection — on feature availability data (populated vs expected feature counts) in a single low-cost call. It is best suited for batch sizes up to 200 rows and situations where no live lookup, external enrichment, or predictive modelling is required. It is more cost-efficient than calling separate audit, normalisation, and change-detection services individually.

## Known failure modes

- Submitting more than 200 rows will be rejected (maxItems constraint)
- Missing required fields (row_id, date, populated_features, expected_features) will cause validation errors
- Malformed date strings not matching YYYY-MM-DD pattern will fail schema validation
- Numeric values outside the allowed range (-1e12 to 1e12) will be rejected
- Payment failure or insufficient USDC balance will prevent the call from completing

## How this service works

Feature availability — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports returned together: (1) a record quality audit highlighting issues such as nulls, out-of-range values, or missing fields in the feature availability rows; (2) a string normalisation report identifying and standardising inconsistent string representations; (3) an adjacent row change report detecting significant differences between consecutive rows in the chosen numeric field (populated_features or expected_features). All reports are computed locally 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-record-quality-audit-string-3908bf23/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)