google-web-search

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Enables grounded question answering by automatically executing the Google Search tool within Gemini models. Use when the required information is recent (post knowledge cutoff) or requires verifiable citation.

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Sourceaaaaqwq/claude-code-skills

Added on2026-02-14

NPX Install

npx skill4agent add aaaaqwq/claude-code-skills google-web-search

SKILL.md Content

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Google Web Search

Overview

This skill provides the capability to perform real-time web searches via the Gemini API's

google_search

grounding tool. It is designed to fetch the most current information available on the web to provide grounded, citable answers to user queries.

Key Features:

Real-time web search via Gemini API
Grounded responses with verifiable citations
Configurable model selection
Simple Python API

Usage

This skill exposes the Gemini API's

google_search

tool. It should be used when the user asks for real-time information, recent events, or requests verifiable citations.

Execution Context

The core logic is in

scripts/example.py

. This script requires the following environment variables:

GEMINI_API_KEY (required): Your Gemini API key
GEMINI_MODEL (optional): Model to use (default:
```
gemini-2.5-flash-lite
```
)

Supported Models:

```
gemini-2.5-flash-lite
```
(default) - Fast and cost-effective
```
gemini-3-flash-preview
```
- Latest flash model
```
gemini-3-pro-preview
```
- More capable, slower
```
gemini-2.5-flash-lite-preview-09-2025
```
- Specific version

Python Tool Implementation Pattern

When integrating this skill into a larger workflow, the helper script should be executed in an environment where the

google-genai

library is available and the

GEMINI_API_KEY

is exposed.

Example Python invocation structure:

python

from skills.google-web-search.scripts.example import get_grounded_response

# Basic usage (uses default model):
prompt = "What is the latest market trend?"
response_text = get_grounded_response(prompt)
print(response_text)

# Using a specific model:
response_text = get_grounded_response(prompt, model="gemini-3-pro-preview")
print(response_text)

# Or set via environment variable:
import os
os.environ["GEMINI_MODEL"] = "gemini-3-flash-preview"
response_text = get_grounded_response(prompt)
print(response_text)

Troubleshooting

If the script fails:

Missing API Key: Ensure
```
GEMINI_API_KEY
```
is set in the execution environment.
Library Missing: Verify that the
```
google-genai
```
library is installed (
```
pip install google-generativeai
```
).
API Limits: Check the API usage limits on the Google AI Studio dashboard.
Invalid Model: If you set
```
GEMINI_MODEL
```
, ensure it's a valid Gemini model name.
Model Not Supporting Grounding: Some models may not support the
```
google_search
```
tool. Use flash or pro variants.