gemini-vision

name

gemini-vision

description

Analyze images using Google Gemini Flash vision model ('Nano Banana'). MANDATORY TRIGGER: Use this skill whenever the user says 'analiza imagen', 'nano banana', 'gemini vision', 'qué ves en la imagen', 'describe la imagen', or pastes/drags an image into chat AND requests visual analysis. Preferred over image-analyzer when Gemini is available (GEMINI_API_KEY set). Supports single images, batch directories, VS Code chat images, and custom prompts. Use when this capability is needed.

Gemini Vision — "Nano Banana"

Analyze images using Google Gemini Flash (vision model) for detailed visual understanding, layout analysis, text detection, and defect identification.

When to Use

ALWAYS use this when:

• User says "analiza imagen", "nano banana", "qué ves", "describe la imagen"
• User drags/pastes an image into VS Code chat
• You need to "see" an image to make decisions (layout, overlap, colors, etc.)
• Visual verification of generated documents (PDF, DOCX, slides)

Priority over image-analyzer: If GEMINI_API_KEY is available, prefer this skill over the GPT-4o-based image-analyzer. Falls back to image-analyzer if no Gemini key.

Auth

Requires GEMINI_API_KEY in env or ~/.env. Get one at: https://aistudio.google.com/apikey

Quick Reference

SCRIPT=~/.claude/skills/gemini-vision/scripts/gemini_vision.py

# Load API key from ~/.env
export $(grep GEMINI_API_KEY ~/.env | xargs)

# Describe one image
python3 "$SCRIPT" photo.jpg

# Custom prompt for specific analysis
python3 "$SCRIPT" footer.png --prompt "Is the blue line overlapping the date text?"

# Analyze latest VS Code chat image
python3 "$SCRIPT" --vscode-latest --prompt "Describe what you see"

# Batch analyze directory
python3 "$SCRIPT" photos/ --batch

# JSON output
python3 "$SCRIPT" img1.png img2.png --json

# Use specific model (default: gemini-2.0-flash)
python3 "$SCRIPT" img.png --model gemini-2.5-flash

VS Code Chat Images

When users drag images into VS Code Copilot Chat:

~/.var/app/com.visualstudio.code/config/Code/User/workspaceStorage/vscode-chat-images/image-<timestamp>.png

Use --vscode-latest to auto-find the most recent one:

python3 "$SCRIPT" --vscode-latest --prompt "What do you see?"

Workflow Integration

1. Document Verification (PDF/DOCX)

After generating a PDF or DOCX, render to image and verify:

# Render PDF page to PNG
flatpak-spawn --host pdftoppm -f 1 -l 1 -r 150 -png document.pdf preview
# Analyze with Gemini
python3 "$SCRIPT" preview-1.png --prompt "Check the footer: is the date text overlapping the blue line?"

2. Before/After Comparison

python3 "$SCRIPT" before.png after.png --prompt "Compare these two images. What changed? Are there improvements or regressions?"

3. Architecture Diagram Review

python3 "$SCRIPT" diagram.svg --prompt "Review this architecture diagram for completeness and accuracy"

Models Available

Model	Best For	Speed
gemini-2.0-flash	General vision, fast	Fast
gemini-2.5-flash	Complex analysis	Medium
gemini-2.5-pro	Highest quality	Slower

Default: gemini-2.0-flash (best speed/quality balance)

Fallback Chain

1. Gemini Flash (this skill) — preferred when GEMINI_API_KEY available
2. GPT-4o (image-analyzer skill) — fallback when OPENAI_API_KEY or GITHUB_TOKEN available
3. Pixel analysis (Pillow) — last resort, programmatic color/position analysis

Lessons Learned

• Gemini Flash is particularly good at detecting text overlap, layout issues, and spatial relationships in documents
• For PDF footer verification, use specific prompts like "Is the blue line overlapping the date text?" rather than generic "describe"
• VS Code Flatpak stores chat images at a non-standard path — use --vscode-latest to handle automatically
• Always export $(grep GEMINI_API_KEY ~/.env | xargs) before calling if key isn't in environment

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Source: CarlosCaPe/octorato — distributed by TomeVault.