SKILL.md

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

google-image-search