# Feature Availability Analytics: Record Quality Audit + Exact Row Deduplication + Base-100 Series

> Feature Availability Analytics: Record Quality Audit + Exact Row Deduplication + Base-100 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).

Runs three analytical reports on feature availability records: record quality audit, exact row deduplication, and a base-100 index series — all computed locally against supplied data, no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-218?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-exact-row-d07bf37f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_JW9mUA4M4gcCFgaAPwzV_

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-exact-row-d07bf37f -d '<json body>'
```

Example prompt: Can you run all three reports — record quality audit, exact row deduplication, and base-100 index series — on this feature availability data I'm uploading? It has rows with row_id, dataset_group, date, populated_features, and expected_features fields, and I want the base-100 index based on the populated_features column starting from row index 0.

## When to prefer this

Choose this endpoint when you need simultaneous data quality auditing, deduplication detection, and base-100 normalization on feature availability records in a single low-cost call. It is ideal for offline/batch data QA workflows on ML feature store logs or similar tabular datasets with populated vs expected feature counts, when no live external lookup or prediction is needed and all analysis is self-contained on supplied data.

## Known failure modes

- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds maximum batch size
- Invalid date format (non YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, populated_features, expected_features) — validation error
- base_index out of range (>= number of rows) — computation error
- Null values in both populated_features and expected_features may degrade index series computation

## How this service works

Feature availability — Record quality audit + Exact row deduplication + Base-100 series: Record quality audit + Exact row deduplication + Base-100 series 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

Returns three independent analytical reports computed locally against the supplied rows: (1) a record quality audit flagging nulls, anomalies, or incomplete records; (2) an exact row deduplication report identifying identical records; and (3) a base-100 index series normalizing the chosen numeric field (populated_features or expected_features) relative to the specified base row index. No external data is fetched.

## 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-record-quality-audit-exact-row-d07bf37f/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)