google-image-search

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Search and download images via Google Custom Search API with LLM-powered selection. This skill should be used when finding images for articles, presentations, research documents, or enriching Obsidian notes with relevant visuals. Supports simple queries, batch processing from JSON config, automatic config generation from terms, and full note enrichment with automatic image insertion below headings.

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Sourceglebis/claude-skills
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NPX Install

npx skill4agent add glebis/claude-skills google-image-search

Tags

Translated version includes tags in frontmatter
google-image-searchllm-image-selectionobsidian-integrationbatch-image-processingapi-image-retrieval

SKILL.md Content

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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:
bash
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:
bash
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:
bash
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:
bash
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

OptionDescription
--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:
json
{
  "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

FilePurpose
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