# Feature Availability Analytics: Record Quality Audit + String Normalisation + Exact Row Deduplication

> Feature Availability Analytics: Record Quality Audit + String Normalisation + Exact Row Deduplication 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 local analytical reports on feature availability records — record quality audit, string normalisation, and exact row deduplication — for up to 200 rows of populated/expected feature data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-191?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-bf549948
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_lsebDHjMq-5i-Af921rah

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-bf549948 -d '<json body>'
```

Example prompt: Can you run a quality audit, string normalisation, and exact deduplication on my feature availability records — I have rows with row_id, dataset_group, date, populated_features, and expected_features fields, up to around 150 rows across groups like 'group-a' and 'group-b'?

## When to prefer this

Choose this endpoint when you need three standard data quality operations — quality audit, string normalisation, and exact deduplication — applied together to feature availability records in a single low-cost call. It is ideal for pre-ingestion data cleaning pipelines, routine quality checks on feature tracking datasets, or any scenario where populated vs expected feature counts need validation across up to 200 dated rows. It does not perform ML-based fuzzy deduplication, external enrichment, or predictive analytics.

## Known failure modes

- Exceeding 200 rows will be rejected (maxItems: 200)
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) will cause validation errors
- Date strings not matching YYYY-MM-DD pattern will fail schema validation
- populated_features or expected_features values outside ±1,000,000,000,000 will be rejected
- Empty rows array (minItems: 1) will be rejected
- Payment failure or insufficient USDC balance will prevent execution

## How this service works

Feature availability — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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 delivered together: (1) a record quality audit flagging missing, null, or out-of-range values in the feature availability rows; (2) a string normalisation report identifying and correcting inconsistencies in text fields like dataset_group; (3) an exact row deduplication report identifying and removing identical rows. All analysis is performed locally with no external lookups or predictions.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-record-quality-audit-string-bf549948/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)