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

(The OPENROUTER_API_KEY=... spelling works too — see the plugin note below.)

OpenRouter / llm-openrouter caveat

The llm-openrouter plugin only registers its models when it can read a key named OPENROUTER_KEY (env var or llm keys set openrouter). With only OPENROUTER_API_KEY set, llm models list shows zero OpenRouter models and every openrouter/... name fails with Unknown model — the script then silently falls back to keyword scoring. llm_select.py sets both names for the subprocess, so this is handled; if selection ever regresses, check OPENROUTER_KEY first:

llm models list | grep -c openrouter   # 458+ means the plugin registered its models

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

2. 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

3. 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

4. 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:

1. Detects Obsidian vault and attachments folder
2. Uses LLM to extract visual-worthy terms from note
3. Searches for images for each term
4. Downloads best images to attachments folder
5. Inserts image embeds below relevant headings
6. Creates backup before modifying note

Key Options

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