Claude-skill-registry gemini-consultant

Get a second opinion from Gemini 3 Pro with Google Search grounding and vision. Use when you need real-time web information, want to verify facts, need a different perspective on a technical question, want to consult another AI model, or need to analyze images.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/gemini-consultant" ~/.claude/skills/majiayu000-claude-skill-registry-gemini-consultant && rm -rf "$T"

manifest: skills/data/gemini-consultant/SKILL.md

Gemini Consultant

Get a second opinion from Google's Gemini 3 Pro (

gemini-3-pro-preview

) with real-time Google Search grounding and vision capabilities.

Prerequisites

The user must have

GEMINI_API_KEY

environment variable set with a valid Google AI API key.

Usage

The script is located in the same directory as this SKILL.md file. Run it with

uv run

uv run /path/to/skills/gemini-consultant/consult.py "your question here"

When this skill is invoked, locate

consult.py

in the skill directory and run it.

Parameters

Parameter	Required	Description
`question`	Yes	The question to ask Gemini
`-c` , `--context`	No	Additional context to include (code snippets, background info)
`-i` , `--image`	No	Image file(s) to analyze (can be used multiple times)
`--media-resolution`	No	Image resolution: `low` (280 tokens), `medium` (560, default), `high` (1120), `ultra_high`
`--no-search`	No	Disable Google Search grounding (use pure model knowledge)
`--thinking`	No	Reasoning depth: `low` (faster) or `high` (deeper, default)

Examples

Simple question with web search:

uv run consult.py "What is the latest version of Python and its new features?"

Question with context:

uv run consult.py "What could cause this error?" -c "TypeError: Cannot read property 'map' of undefined"

Fast response without deep reasoning:

uv run consult.py "Quick summary of REST vs GraphQL" --thinking low

Without web search (pure model knowledge):

uv run consult.py "Explain the CAP theorem" --no-search

Analyze an image:

uv run consult.py "What's in this image?" -i screenshot.png

Analyze multiple images:

uv run consult.py "Compare these two diagrams" -i diagram1.png -i diagram2.png

High-resolution image analysis (for fine text or small details):

uv run consult.py "Read the text in this image" -i document.png --media-resolution high

When to Use

Real-time information: Current events, latest releases, recent updates
Fact verification: Double-check information with web sources
Second opinion: Get an alternative perspective on technical decisions
Web research: Find current documentation, tutorials, or solutions
Image analysis: Analyze screenshots, diagrams, photos, or any visual content
Compare images: Analyze multiple images together

Output

The script prints:

The model's response
Sources/citations from Google Search (when grounding is enabled)