# Feature Availability Duplicate Key Report + Explicit Missing-Value Fill

> Feature Availability Duplicate Key Report + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).

Runs two local analytical reports on feature availability records: a duplicate key audit and an explicit missing-value fill using zero, mean, or median imputation, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-070?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-duplicate-key-report-explicit-missing-value-fill-7e5e792b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_EYUJQJhhEI_7KexTnqYSo

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-duplicate-key-report-explicit-missing-value-fill-7e5e792b -d '<json body>'
```

Example prompt: Can you run a duplicate key report and fill in any missing populated_features values using median imputation on my feature availability records — I have about 150 rows with row_id, dataset_group, date, populated_features, and expected_features fields?

## When to prefer this

Choose this endpoint when you need a fast, low-cost ($0.01 USDC) dual-report data quality check on feature availability records — specifically combining duplicate key detection with missing-value imputation in a single call. Ideal for data pipeline preprocessing steps where you want to catch duplicate row keys and fill nulls before model training or downstream analytics, without requiring live external lookups or professional assessment.

## Known failure modes

- Fewer than 1 row provided — minimum 1 row required
- More than 200 rows submitted — exceeds the maxItems limit
- Invalid date format (must be YYYY-MM-DD pattern)
- row_id missing or empty string — required field
- Numeric values outside the allowed range (-1e12 to 1e12)
- Invalid enum value for field or method parameters
- Malformed JSON input body

## How this service works

Feature availability — Duplicate key report + Explicit missing-value fill: Duplicate key report + Explicit missing-value fill 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 reports: (1) a duplicate key report identifying any rows sharing the same key fields, and (2) an explicit missing-value fill report where null values in the selected field (populated_features or expected_features) are replaced using the chosen method (zero, mean, or median), all 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-duplicate-key-report-explicit-missing-value-fill-7e5e792b/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)