# Extract the next new anonaly in a dataset — Anomaly Detection

Botize method — `btziforest.iforest` (trigger).

Triggers with the next new anomaly detected in a dataset using Isolation Forest.

This is a trigger: it starts the task. A task has exactly one.

## Fields you set

The field's internal name, its type and the values it accepts. These fields accept `{{...}}` tags from earlier steps.

- `url_train_dataset` *(string)* — Train Dataset URL (CSV format)
- `train_key` *(boolean, default: "")* — Exclude CSV first columns (use it as a Key)
- `url_predict_dataset` *(string)* — Predict Dataset URL (CSV format)
- `predict_key` *(boolean, default: "")* — Exclude CSV first columns (use it as a Key)

*(Fields derived from the configuration form.)*

## Output variables

Data this step makes available. Reference them in later steps with the double-brace tag.

- `{{train_duration}}` *(int)* — Train duration in seconds
- `{{url_anomalies_dataset}}` *(url)* — Anomalies Dataset URL
- `{{total_instances}}` *(int)* — Total number of instances
- `{{total_anomalies}}` *(int)* — Total number of anomalies

## This app

Every trigger and action for Anomaly Detection: https://botize.com/en/app/btziforest.md

## How to build this

An AI assistant (Claude, ChatGPT, Cursor…) can create and edit these tasks in the user's account through the Botize MCP connector, included in every plan:

    https://webhook.botize.es/mcp

- Connection guide: https://botize.com/en/mcp
- Agent map: https://botize.com/llms.txt
- New account: https://botize.com/en/join (500 free operations, no card)

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Machine-readable summary for AI assistants. Human version: https://botize.com/en/method/btziforest/iforest