# Transformation Throughput — Numeric Row Ordering + IQR Outlier Flags

> Transformation Throughput — Numeric Row Ordering + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).

Ranks transformation throughput records by processed_rows or duration_seconds and flags statistical outliers using the interquartile range (IQR) method, producing two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-119?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transformation-throughput-numeric-row-ordering-iqr-outlier-flags-5d1c3ddc
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_lyYTNqbs0jW17RhvtZ53x

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 transformation-throughput-numeric-row-ordering-iqr-outlier-flags-5d1c3ddc -d '<json body>'
```

Example prompt: Analyze my transformation throughput records — I have up to 50 rows each with a row_id, dataset_group, date, processed_rows, and duration_seconds — rank them by processed_rows and flag any IQR outliers across both metrics.

## When to prefer this

Choose this endpoint when you have tabular transformation throughput records (ETL job logs, pipeline run metrics) and need both a sorted ranking and IQR-based outlier detection in a single call, without requiring any external data, live predictions, or professional assessment. It is best suited for offline batch analysis of up to 200 rows where two simultaneous statistical reports on processed_rows and duration_seconds are needed cost-effectively.

## Known failure modes

- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — request rejected
- Missing required fields (row_id, date, processed_rows, duration_seconds) — schema validation failure
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- Non-numeric values in processed_rows or duration_seconds — type error
- Invalid field enum value (not 'processed_rows' or 'duration_seconds') — enum validation error
- All values identical — IQR is zero, no outliers flagged (degenerate case)

## How this service works

Transformation throughput — Numeric row ordering + IQR outlier flags: Numeric row ordering + IQR outlier flags for supplied transformation throughput records (processed_rows, duration_seconds). 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) rows ordered numerically by the selected field (processed_rows or duration_seconds), and (2) IQR-based outlier flags identifying records that fall below Q1−1.5×IQR or above Q3+1.5×IQR. No external lookups or predictions are made; analysis is entirely local to the supplied data.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "processed_rows",
     "duration_seconds"
    ],
    "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
     },
     "processed_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "duration_seconds": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "processed_rows": 10,
     "duration_seconds": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "processed_rows": 20,
     "duration_seconds": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "processed_rows": 15,
     "duration_seconds": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "processed_rows": 30,
     "duration_seconds": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "processed_rows",
    "duration_seconds"
   ],
   "type": "string",
   "default": "processed_rows"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/transformation-throughput-numeric-row-ordering-iqr-outlier-flags-5d1c3ddc/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)