Vision Tool

💡 Examples

Basic usage

# From any OpenClaw channel
exec: python3 /path/to/vision-tool/main.py /path/to/image.jpg
With custom prompt
exec: python3 /path/to/vision-tool/main.py /path/to/image.jpg --prompt "Describe this image"
Debug output
exec: python3 /path/to/vision-tool/main.py /path/to/image.jpg --debug

Channel-specific examples

WeChat Channel:

# When receiving an image
exec: python3 /path/to/vision-tool/main.py "$IMAGE_PATH"

Telegram Channel:

# Reply to photo messages
exec: python3 /path/to/vision-tool/main.py "/path/to/telegram_photo.jpg"

Discord Channel:

# Process attachments
exec: python3 /path/to/vision-tool/main.py "./discord_attachment.jpg"

⚙️ Configuration

1. Ollama service: ollama serve (must be running) 2. qwen3.5:4b model: ollama pull qwen3.5:4b 3. Python 3.8+: Required for running the skill

Install the skill

clawhub install vision-tool

Development Setup (For Contributors)

If you want to contribute or modify the skill, see CONTRIBUTING.md for detailed development instructions.

Basic setup:

# Clone the repository
git clone https://github.com/HuRuilizhen/vision-tool
cd vision-tool
Set up development environment
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
Run tests
python3 -m pytest tests/

📋 Tips & Best Practices

Common Issues

1. Ollama not running: Run ollama serve first 2. Model not installed: Run ollama pull qwen3.5:4b 3. Image path incorrect: Use absolute paths or correct relative paths 4. Timeout: Model may take 30+ seconds for complex images

Performance Tips

Compress images to under 300KB for faster processing

Use clear, concise prompts

Ensure Ollama has sufficient system resources