Imported from majiayu000/claude-skill-registry-data (ai-ml/gemini-vision-einverne-dotfiles/SKILL.md). Install upstream with npx skills add majiayu000/claude-skill-registry-data --skill gemini-vision-einverne-dotfiles. Copyright stays with the author (MIT).

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
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)

   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

# 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+)

python scripts/analyze-image.py image.jpg "Detect all objects" --model gemini-2.0-flash

Multi-Image Comparison

python scripts/analyze-image.py img1.jpg img2.jpg "What's different between these?"

File Upload (for large files or reuse)

# 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

# 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