carloscape/octorato/image-analyzer

Image Analyzer

Analyze images using GPT-4o vision via GitHub Models API or OpenAI API. Supports single files, globs, directories, and batch mode.

MANDATORY: When User References an Image

ALWAYS do this when the user mentions an image, screenshot, photo, or pastes one:

1. Check for VS Code chat images (drag-and-drop into chat):

``bash ls -lt ~/.var/app/com.visualstudio.code/config/Code/User/workspaceStorage/vscode-chat-images/ 2>/dev/null | head -5 `` Pick the most recent file matching the conversation timestamp.

1. Check for explicit file path in the user message.

1. Check common screenshot locations:

``bash ls -lt ~/Pictures/Screenshots/ ~/Desktop/ 2>/dev/null | head -5 ``

1. Run the analyzer on the found image:

``bash python3 ~/.claude/skills/image-analyzer/scripts/analyze_image.py /path/to/image.png --prompt "Describe what you see in detail" ``

NEVER say "I can't see images" — you CAN, via this script. Use it.

VS Code Chat Image Path Pattern

When users drag images into VS Code Copilot Chat, they land at:

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

To find the latest one:

ls -t ~/.var/app/com.visualstudio.code/config/Code/User/workspaceStorage/vscode-chat-images/ | head -1

Auth Priority

1. OPENAI_API_KEY → OpenAI API
2. GITHUB_TOKEN / GH_TOKEN → GitHub Models API
3. gh auth token → GitHub Models API

Quick Reference

SCRIPT=~/.claude/skills/image-analyzer/scripts/analyze_image.py
 
# Describe one image
python3 "$SCRIPT" photo.jpg
 
# Analyze VS Code chat image (latest)
LATEST=$(ls -t ~/.var/app/com.visualstudio.code/config/Code/User/workspaceStorage/vscode-chat-images/ | head -1)
python3 "$SCRIPT" ~/.var/app/com.visualstudio.code/config/Code/User/workspaceStorage/vscode-chat-images/"$LATEST" --prompt "Describe what you see"
 
# Classify into real-estate categories (JSON output)
python3 "$SCRIPT" photo.jpg --classify
 
# Batch classify all images in a directory (single API call, faster)
python3 "$SCRIPT" photos/ --classify --batch
 
# Custom prompt
python3 "$SCRIPT" photo.jpg --prompt "What room is this? One word."
 
# Detect duplicates in a gallery screenshot
python3 "$SCRIPT" screenshot.png --prompt "Identify any duplicate or very similar photos in this gallery"
 
# Multiple files with JSON output
python3 "$SCRIPT" img1.jpg img2.jpg --json
 
# Glob pattern
python3 "$SCRIPT" "photos/*.jpeg" --classify --batch

Real-Estate Categories

The --classify flag maps images to: FACHADA, COCHERA, SALA, SALA_COMEDOR, COMEDOR, COCINA, RECAMARA, BAÑO, VESTIDOR, AREA_SOCIAL, ESCALERAS, TERRAZA, JARDIN, PATIO, AREA_LAVADO, CUARTO_SERVICIO, OFICINA, PANELES_SOLARES, PASILLO, FUENTE, ALBERCA, AMENIDADES, BODEGA, OTRO.

Output: {"category": "...", "title": "...", "description": "...", "file": "..."}

Workflow: Classify New Property Photos

1. Run: python3 "$SCRIPT" /path/to/new/photos/ --classify --batch
2. Parse JSON output to get category + title per image
3. Rename files: CATEGORY_NN.jpeg
4. Copy to public/realestate/<property>/
5. Update ficha_tecnica.json with new entries

Options

Rate Limits (GitHub Models API)

• 10 requests per 60 seconds
• Batch mode maxes at ~8000 tokens (8-10 images)
• For large batches: process in groups of 5 with 12s pauses