prepare-environment

{
  "task": "Train ML model",
  "base": "python:3.11-slim",
  "packages": "numpy pandas scikit-learn"
}

Prepare an environment for: Train ML model

**Step 1: CHECK for Existing Environment**

Search for prepared environments:
```json
// list_images
{"tag_prefix": "common/"}

// import_image
{"registry_url": "docker://docker.io/python:3.11-slim"}

// run
{
  "command": "pip install numpy pandas scikit-learn",
  "image": "<result_image>",
  "disposable": false
}

// set_tag
{"image_uuid": "<result_image>", "tag": "common/numpy-env/python:3.11-slim"}

// run
{"command": "<task command>", "image": "tag:common/numpy-env/python:3.11-slim"}

## Tag Generation

The prompt automatically generates a tag following the convention:

- **scope**: Uses `project` parameter if provided, otherwise `common`
- **purpose**: Derived from first package name (e.g., `numpy-env`) or `custom-env`
- **base:tag**: From `base` parameter

## Example Usage

### ML Development Environment

```json
{
  "prompt": "prepare-environment",
  "args": {
    "task": "Run data analysis notebook",
    "packages": "jupyter pandas matplotlib seaborn"
  }
}

{
  "prompt": "prepare-environment",
  "args": {
    "task": "Run API tests",
    "project": "myproject",
    "packages": "pytest requests httpx"
  }
}

{
  "prompt": "prepare-environment",
  "args": {
    "task": "Compile Rust project",
    "base": "rust:1.75-slim"
  }
}