gemini-llm

name	gemini-llm
description	Invoke Google Gemini 3 Pro for text generation, reasoning, and code tasks using the Python google-genai SDK. Supports gemini-3-pro-preview (best multimodal), gemini-2.5-pro (reasoning), and gemini-2.5-flash (fast).

Gemini LLM Skill

Invoke Google Gemini models for text generation, reasoning, code analysis, and complex tasks using the Python google-genai SDK.

Available Models

Model ID	Description	Best For
gemini-3-pro-preview	Best multimodal understanding	Complex reasoning, analysis
gemini-2.5-pro	Advanced thinking model	Deep reasoning, planning
gemini-2.5-flash	Fast and capable	Quick tasks, high throughput
gemini-2.5-flash-lite	Fastest, cost-efficient	Simple tasks, bulk processing

Configuration

API Key Location: C:\Users\USERNAME\env (GEMINI_API_KEY)

Default API Key: ${GEMINI_API_KEY}

Usage

Basic Text Generation

python -c "
from google import genai
client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents='YOUR_PROMPT_HERE'
)
print(response.text)
"

With System Instructions

python -c "
from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents='YOUR_PROMPT_HERE',
    config=types.GenerateContentConfig(
        system_instruction='You are a helpful coding assistant.',
        temperature=0.7,
        max_output_tokens=8192
    )
)
print(response.text)
"

Streaming Response

python -c "
from google import genai
client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
for chunk in client.models.generate_content_stream(
    model='gemini-3-pro-preview',
    contents='YOUR_PROMPT_HERE'
):
    print(chunk.text, end='', flush=True)
print()
"

Workflow

When this skill is invoked:

1. Parse the user request to determine:

   • The prompt/task to send to Gemini
   • Which model to use (default: gemini-3-pro-preview)
   • Any configuration options (temperature, max tokens, system instruction)

2. Select the appropriate model:

   • Complex reasoning/analysis → gemini-3-pro-preview
   • Deep planning/thinking → gemini-2.5-pro
   • Quick responses → gemini-2.5-flash
   • Bulk/simple tasks → gemini-2.5-flash-lite

3. Execute the Python command using Bash tool:

   python -c "
   from google import genai
   client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
   response = client.models.generate_content(
       model='MODEL_ID',
       contents='''PROMPT'''
   )
   print(response.text)
   "
   

4. Return the response to the user

Example Invocations

Code Review

python -c "
from google import genai
client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents='''Review this Python code for bugs and improvements:

def calculate_total(items):
    total = 0
    for item in items:
        total += item.price * item.quantity
    return total
'''
)
print(response.text)
"

Explain Concept

python -c "
from google import genai
client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='Explain async/await in Python in simple terms'
)
print(response.text)
"

Generate Code

python -c "
from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents='Write a Python function to merge two sorted lists',
    config=types.GenerateContentConfig(
        system_instruction='You are an expert Python developer. Write clean, efficient, well-documented code.',
        temperature=0.3
    )
)
print(response.text)
"

Multi-turn Conversations

For conversations with history:

python -c "
from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

history = [
    types.Content(role='user', parts=[types.Part(text='What is Python?')]),
    types.Content(role='model', parts=[types.Part(text='Python is a high-level programming language...')]),
    types.Content(role='user', parts=[types.Part(text='How do I install it?')])
]

response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents=history
)
print(response.text)
"

Error Handling

The skill handles common errors:

• 404 Not Found: Model not available - fall back to gemini-2.5-pro
• Rate Limiting: Wait and retry with exponential backoff
• Token Limits: Truncate input or use streaming for large outputs

Notes

• Gemini 3 Pro is NOT available via the Gemini CLI (v0.17.1) - must use Python SDK
• The thought_signature warning can be ignored - it's internal model metadata
• For long prompts, use triple quotes and escape special characters
• Maximum context: varies by model (check documentation)

Tools to Use

• Bash: Execute Python commands
• Read: Load files to include in prompts
• Write: Save Gemini responses to files