# Transformation Throughput — String Normalisation + IQR Outlier Flags

> Transformation Throughput — String Normalisation + 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).

Normalises string fields and flags IQR-based outliers for transformation throughput records (processed_rows, duration_seconds) across up to 200 rows, returning 2 independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-032?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-string-normalisation-iqr-outlier-flags-fe22af7d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_RRVOZ4-GaUa5wziGPdB0i

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-string-normalisation-iqr-outlier-flags-fe22af7d -d '<json body>'
```

Example prompt: Can you run string normalization and IQR outlier detection on my transformation throughput data — here are 5 pipeline runs with row_id, dataset_group, date, processed_rows, and duration_seconds — flag which records are outliers using processed_rows as the target field?

## When to prefer this

Choose this endpoint when you need both string normalization and IQR-based statistical outlier detection on ETL or data pipeline throughput records in a single call, without requiring external data lookups, ML inference, or live data connections. Ideal for data quality pipelines, pipeline monitoring dashboards, or pre-report data cleaning workflows where processed_rows or duration_seconds anomaly detection is needed on batches up to 200 rows.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems limit
- Missing required fields (row_id, date, processed_rows, duration_seconds) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- All values for selected field are null — IQR computation cannot proceed
- Numeric values outside ±1e12 range rejected by schema bounds
- Malformed JSON payload returns 400-level error

## How this service works

Transformation throughput — String normalisation + IQR outlier flags: String normalisation + 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

Two independent analytical reports per call: one covering string normalisation of the dataset_group and related string fields, and one flagging per-row IQR outlier status for the selected numeric field (processed_rows or duration_seconds), including computed IQR thresholds and per-row flags across up to 200 input records.

## 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-string-normalisation-iqr-outlier-flags-fe22af7d/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)