# {{ display_name }}

{{ description }}

## Overview

This AI analysis automation uses LLM to analyze content through multiple stages:
summarization, sentiment analysis, and entity/insight extraction.

## Analysis Stages

1. **Summarize** - Generate a concise summary of the content
2. **Analyze** - Perform sentiment and topic analysis
3. **Extract** - Extract key entities and actionable insights

## Installation

```bash
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

## Usage

### Local Development

```bash
# Run the automation
torivers run --input sample_input.json

# Run tests
torivers test

# Validate before submission
torivers validate
```

### Input Schema

```json
{
  "content": "Content to analyze...",
  "analysis_type": "comprehensive",
  "options": {
    "language": "en",
    "max_tokens": 1000
  }
}
```

### Output Schema

```json
{
  "summary": "Content summary...",
  "sentiment": {
    "overall": "positive",
    "confidence": 0.85,
    "aspects": [...]
  },
  "entities": [
    {"name": "...", "type": "...", "relevance": 0.9}
  ],
  "insights": [
    {"insight": "...", "importance": "high", "category": "..."}
  ]
}
```

## Project Structure

```
{{ name }}/
├── automation.yaml    # Automation manifest
├── main.py            # Main automation class
├── state.py           # State definition
├── nodes/
│   ├── __init__.py
│   ├── summarize.py   # Content summarization
│   ├── analyze.py     # Sentiment/topic analysis
│   └── extract.py     # Entity/insight extraction
├── tests/
│   ├── __init__.py
│   └── test_automation.py
├── .gitignore
├── requirements.txt
├── README.md
└── sample_input.json
```

## Development

1. Customize prompts in the node modules
2. Add or modify analysis stages in `main.py`
3. Update state schema in `state.py` as needed
4. Add tests in `tests/test_automation.py`

## Author

{{ author_name }} <{{ author_email }}>
