微信扫一扫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)