Google Image Search Skill
Search for images using Google Custom Search API with intelligent scoring and LLM-based selection.
When to Use Finding images to illustrate technical articles or research Adding visuals to presentations Enriching Obsidian notes with relevant images Batch image search for multiple topics Generating image search configs from plain text lists Requirements Google Custom Search API key and Search Engine ID OpenRouter API key (for LLM selection) llm CLI installed at /opt/homebrew/bin/llm
Store credentials in .env:
Google-Custom-Search-JSON-API-KEY=your_key Google-Custom-Search-CX=your_cx OPENROUTER_API_KEY=your_openrouter_key
Modes of Operation 1. Simple Query
Search for a single term:
python3 ~/.claude/skills/google-image-search/scripts/google_image_search.py \ --query "neural interface wearable device" \ --output-dir ./images \ --num-results 5
- Batch Processing
Process multiple queries from JSON config:
python3 ~/.claude/skills/google-image-search/scripts/google_image_search.py \ --config image_queries.json \ --output-dir ./images \ --llm-select
- Generate Config from Terms
Create JSON config from a list of terms using LLM:
python3 ~/.claude/skills/google-image-search/scripts/google_image_search.py \ --generate-config \ --terms "AlterEgo wearable" "sEMG electrodes" "BCI headset" \ --output my_queries.json
- Enrich Obsidian Note
Extract visual terms from note, find images, and insert below headings:
python3 ~/.claude/skills/google-image-search/scripts/google_image_search.py \ --enrich-note ~/Brains/brain/Research/neural-interfaces.md
This mode:
Detects Obsidian vault and attachments folder Uses LLM to extract visual-worthy terms from note Searches for images for each term Downloads best images to attachments folder Inserts image embeds below relevant headings Creates backup before modifying note Key Options Option Description --query TEXT Simple single query --config FILE JSON config for batch --generate-config Generate config from --terms --enrich-note FILE Enrich Obsidian note --output-dir DIR Where to save images --urls-only Return URLs only, no download --llm-select Use LLM to pick best image (default: on) --no-llm-select Disable LLM selection --num-results N Results per query (default: 5) --dry-run Show what would be done JSON Config Format
Each entry supports:
{ "id": "unique-id", "heading": "Display Heading", "description": "Context for what image to find", "query": "Google search query", "numResults": 5, "selectionCriteria": "What makes a good image", "requiredTerms": ["must", "have"], "optionalTerms": ["bonus", "terms"], "excludeTerms": ["stock", "clipart"], "preferredHosts": ["official-site.com"], "selectionCount": 2 }
See references/api_config_reference.md for full documentation.
Scoring System
Images are scored based on:
Required terms: -80 if missing, +30 if all present Optional terms: +5 per match Exclude terms: -50 per match Preferred hosts: +25 if trusted, -5 if unknown MIME type: +5 for PNG/JPEG, -10 for GIF Resolution: +10 for high res, -10 for low res File size: -5 if very small LLM Selection
After scoring, LLM picks the best image from top candidates based on:
Title and URL metadata Scoring reasons Selection criteria
The LLM evaluates authenticity, clarity, and relevance for technical audiences.
Obsidian Integration
When in an Obsidian vault:
Auto-detects vault root via .obsidian folder Uses configured attachments folder (default: Attachments) Generates Obsidian-style embeds: ![[image.png|alt text]] Creates backup before modifying notes Script Files File Purpose google_image_search.py Main entry point api.py Google Custom Search API config.py Credentials and config handling download.py Image download with magic bytes evaluate.py Keyword-based scoring llm_select.py LLM selection and term extraction obsidian.py Vault detection and enrichment output.py Markdown output generation