# Feature Availability Analytics — Exact Row Deduplication + Date by Category Pivot

> Feature Availability Analytics — Exact Row Deduplication + Date by Category Pivot 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).

Performs exact row deduplication and date-by-category pivot analysis on supplied feature availability records (populated_features, expected_features) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-052?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-exact-row-deduplication-date-by-c89ee1ef
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Gao9kxMeNf_eTtXOQvqAs

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-exact-row-deduplication-date-by-c89ee1ef -d '<json body>'
```

Example prompt: I have feature availability records for two dataset groups — group-a and group-b — with dates, populated_features, and expected_features columns. Can you run exact row deduplication and a date-by-category pivot on those rows so I can see clean counts and a crosstab summary? Use populated_features as the pivot field.

## When to prefer this

Choose this endpoint when you need fast, offline analytical processing of feature availability records — specifically exact row deduplication combined with a date-by-category pivot — on a small-to-medium dataset (up to 200 rows) without requiring any live data lookups, predictions, or external integrations. It is ideal for data quality checks in ML pipelines, feature store audits, or ETL validation steps where two bundled reports are needed cost-effectively at $0.01 USDC.

## Known failure modes

- Exceeds 200 row limit — request rejected
- Missing required fields (row_id, date, dataset_group, populated_features, expected_features) — validation error
- Invalid date format (must match YYYY-MM-DD) — pattern validation failure
- Field value out of numeric range (±1,000,000,000,000) — schema rejection
- Empty rows array — minItems violation
- Payment not completed or insufficient USDC — 402 response

## How this service works

Feature availability — Exact row deduplication + Date by category pivot: Exact row deduplication + Date by category pivot 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 analytical reports: (1) a deduplicated version of the supplied rows with exact duplicates removed, and (2) a date-by-category pivot table showing the selected field (populated_features or expected_features) broken down by date and dataset_group. Both reports are computed locally from the supplied data 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-exact-row-deduplication-date-by-c89ee1ef/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)