Google Image Search Skill for Claude

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

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:

• 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 |