--- name: gemini-vision description: Guide for implementing Google Gemini API image understanding - analyze images with captioning, classification, visual QA, object detection, segmentation, and multi-image comparison. Use when analyzing images, answering visual questions, detecting objects, or processing documents with vision. license: MIT allowed-tools: - Bash - Read - Write - Edit --- # Gemini Vision API Skill This skill enables Claude to use Google's Gemini API for advanced image understanding tasks including captioning, classification, visual question answering, object detection, segmentation, and multi-image analysis. ## Quick Start ### Prerequisites 1. **Get API Key**: Obtain from [Google AI Studio](https://aistudio.google.com/apikey) 2. **Install SDK**: `pip install google-genai` (Python 3.9+) ### API Key Configuration The skill checks for `GEMINI_API_KEY` in this order: 1. **Process environment variable** (recommended) ```bash export GEMINI_API_KEY="your-api-key" ``` 2. **Skill directory**: `.claude/skills/gemini-vision/.env` ``` GEMINI_API_KEY=your-api-key ``` 3. **Project directory**: `.env` or `.gemini_api_key` in project root **Security**: Never commit API keys to version control. Add `.env` to `.gitignore`. ## Core Capabilities ### Image Analysis - **Captioning**: Generate descriptive text for images - **Classification**: Categorize and identify image content - **Visual QA**: Answer questions about image content - **Multi-image**: Compare and analyze up to 3,600 images ### Advanced Features (Model-Specific) - **Object Detection**: Identify and locate objects with bounding boxes (Gemini 2.0+) - **Segmentation**: Create pixel-level masks for objects (Gemini 2.5+) - **Document Understanding**: Process PDFs with vision (up to 1,000 pages) ## Supported Formats - **Images**: PNG, JPEG, WEBP, HEIC, HEIF - **Documents**: PDF (up to 1,000 pages) - **Size Limits**: - Inline: 20MB max total request size - File API: For larger files - Max images: 3,600 per request ## Available Models - **gemini-2.5-pro**: Most capable, segmentation + detection - **gemini-2.5-flash**: Fast, efficient, segmentation + detection - **gemini-2.5-flash-lite**: Lightweight, segmentation + detection - **gemini-2.0-flash**: Object detection support - **gemini-1.5-pro/flash**: Previous generation ## Usage Examples ### Basic Image Analysis ```bash # Analyze a local image python scripts/analyze-image.py path/to/image.jpg "What's in this image?" # Analyze from URL python scripts/analyze-image.py https://example.com/image.jpg "Describe this" # Specify model python scripts/analyze-image.py image.jpg "Caption this" --model gemini-2.5-pro ``` ### Object Detection (2.0+) ```bash python scripts/analyze-image.py image.jpg "Detect all objects" --model gemini-2.0-flash ``` ### Multi-Image Comparison ```bash python scripts/analyze-image.py img1.jpg img2.jpg "What's different between these?" ``` ### File Upload (for large files or reuse) ```bash # Upload file python scripts/upload-file.py path/to/large-image.jpg # Use uploaded file python scripts/analyze-image.py file://file-id "Caption this" ``` ### File Management ```bash # List uploaded files python scripts/manage-files.py list # Get file info python scripts/manage-files.py get file-id # Delete file python scripts/manage-files.py delete file-id ``` ## Token Costs Images consume tokens based on size: - **Small** (≤384px both dimensions): 258 tokens - **Large**: Tiled into 768×768 chunks, 258 tokens each **Token Formula**: ``` crop_unit = floor(min(width, height) / 1.5) tiles = (width / crop_unit) × (height / crop_unit) total_tokens = tiles × 258 ``` **Example**: 960×540 image = 6 tiles = 1,548 tokens ## Rate Limits Limits vary by tier (Free, Tier 1, 2, 3): - Measured in RPM (requests/min), TPM (tokens/min), RPD (requests/day) - Applied per project, not per API key - RPD resets at midnight Pacific ## Best Practices ### Image Quality - Use clear, non-blurry images - Verify correct image rotation - Consider token costs when sizing ### Prompting - Be specific in instructions - Place text after image for single-image prompts - Use few-shot examples for better accuracy - Specify output format (JSON, markdown, etc.) ### File Management - Use File API for files >20MB - Use File API for repeated usage (saves tokens) - Files auto-delete after 48 hours - Clean up manually when done ### Security - Never expose API keys in code - Use environment variables - Add API key restrictions in Google Cloud Console - Monitor usage regularly - Rotate keys periodically ## Error Handling Common errors: - **401**: Invalid API key - **429**: Rate limit exceeded - **400**: Invalid request (check file size, format) - **403**: Permission denied (check API key restrictions) ## Additional Resources See the `references/` directory for: - **api-reference.md**: Detailed API methods and endpoints - **examples.md**: Comprehensive code examples - **best-practices.md**: Advanced tips and optimization strategies ## Implementation Guide When implementing Gemini vision features: 1. **Check API key availability** using the 3-step lookup 2. **Choose appropriate model** based on requirements: - Need segmentation? Use 2.5+ models - Need detection? Use 2.0+ models - Need speed? Use Flash variants - Need quality? Use Pro variants 3. **Validate inputs**: - Check file format (PNG, JPEG, WEBP, HEIC, HEIF, PDF) - Verify file size (<20MB for inline, >20MB use File API) - Count images (max 3,600) 4. **Handle responses** appropriately: - Parse structured output if requested - Extract bounding boxes for object detection - Process segmentation masks if applicable 5. **Manage files** efficiently: - Upload large files via File API - Reuse uploaded files when possible - Clean up after use ## Scripts Overview All scripts support the 3-step API key lookup: - **analyze-image.py**: Main script for image analysis, supports inline and File API - **upload-file.py**: Upload files to Gemini File API - **manage-files.py**: List, get metadata, and delete uploaded files Run any script with `--help` for detailed usage instructions. --- **Official Documentation**: https://ai.google.dev/gemini-api/docs/image-understanding