gemini-vision

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
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.

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Official Documentation: https://ai.google.dev/gemini-api/docs/image-understanding