name: AI Model Notebook Generator
description: Creates end-to-end Colab notebooks for any new AI model release when user mentions a model name and provider

AI Model Notebook Generator

When to Use This Skill

Trigger this skill when the user:
- Mentions a new AI model name (e.g., "GPT-4o", "Claude opus 4.5", "Gemini 3.0", "DeepSeek V3", "Llama 4.1")
- Asks to create notebooks for a model
- Mentions a provider (OpenAI, Google, Anthropic, OpenRouter, Ollama, etc.)
- Says things like "create notebooks for [model]" or "make a cookbook for [model]"

Setup

!pip install openai langchain-openai

from google.colab import userdata
import openai

client = openai.OpenAI(api_key=userdata.get("OPENAI_API_KEY"))

Basic call

response = client.chat.completions.create(
model="gpt-4o", # or gpt-4o-mini, gpt-4-turbo, etc.
messages=[{"role": "user", "content": "Hello!"}]
)

### Google Gemini Models
```python
# Setup
!pip install google-genai

from google.colab import userdata
from google import genai

client = genai.Client(api_key=userdata.get("GOOGLE_API_KEY"))

# Basic call
response = client.models.generate_content(
    model="gemini-2.5-flash",  # or gemini-2.5-pro, etc.
    contents="Hello!"
)

Anthropic Claude Models

# Setup
!pip install anthropic langchain-anthropic

from google.colab import userdata
import anthropic

client = anthropic.Anthropic(api_key=userdata.get("ANTHROPIC_API_KEY"))

# Basic call
response = client.messages.create(
    model="claude-4-5-sonnet",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)

OpenRouter (Multiple Providers)

# Setup
!pip install openai

from google.colab import userdata
from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=userdata.get("OPENROUTER_API_KEY")
)

# Basic call
response = client.chat.completions.create(
    model="anthropic/claude-4.5-sonnet",  # or openai/gpt-4o, 
    messages=[{"role": "user", "content": "Hello!"}]
)

Ollama (Local Models)

# Setup (requires Ollama running locally)
!pip install ollama

import ollama

# Basic call
response = ollama.chat(
    model="llama3.1",
    messages=[{"role": "user", "content": "Hello!"}]
)

Required Header for ALL Notebooks

Add this at the TOP of every notebook (first markdown cell):

<img src="https://drive.google.com/uc?export=view&id=1wYSMgJtARFdvTt5g7E20mE4NmwUFUuog" width="200">

[![Gen AI Experiments](https://img.shields.io/badge/Gen%20AI%20Experiments-GenAI%20Bootcamp-blue?style=for-the-badge&logo=artificial-intelligence)](https://github.com/buildfastwithai/gen-ai-experiments)
[![Gen AI Experiments GitHub](https://img.shields.io/github/stars/buildfastwithai/gen-ai-experiments?style=for-the-badge&logo=github&color=gold)](http://github.com/buildfastwithai/gen-ai-experiments)

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/[NOTEBOOK_ID])

## Master Generative AI in 8 Weeks
**What You'll Learn:**
- Master cutting-edge AI tools & frameworks
- 6 weeks of hands-on, project-based learning
- Weekly live mentorship sessions
- No coding experience required
- Join Innovation Community

Transform your AI ideas into reality through hands-on projects and expert mentorship.

[Start Your Journey](https://www.buildfastwithai.com/genai-course)

---

Note: Replace [NOTEBOOK_ID] with actual Colab notebook ID.

10 Notebooks to Create

1. Testing & Basics

File: 01_[ModelName]_Testing_Basics.ipynb

2. Advanced Features

File: 02_[ModelName]_Advanced_Features.ipynb

3. Simple RAG

File: 03_[ModelName]_Simple_RAG.ipynb

4. Advanced RAG

File: 04_[ModelName]_Advanced_RAG.ipynb

5. CrewAI Agents

File: 05_[ModelName]_CrewAI_Agents.ipynb

6. Agno Agent Framework

File: 06_[ModelName]_Agno_Agents.ipynb

7. Multimodal RAG

File: 07_[ModelName]_Multimodal_RAG.ipynb

10. Specialized Use Cases

File: 10_[ModelName]_Specialized_UseCases.ipynb

Choose 3-5 based on model capabilities:
- Fine-tuning / Prompt Optimization
- Multimodal Applications
- Domain-Specific (medical, legal, code)
- Evaluation & Benchmarking
- Production Integrations (FastAPI, Streamlit)

Quality Checklist

• [ ] All cells execute successfully
• [ ] No hardcoded API keys (use Colab secrets)
• [ ] Error handling included
• [ ] Clear documentation
• [ ] Performance metrics
• [ ] Cost estimates
• [ ] @BuildFastWithAI branding

File Structure

model-notebooks/
├── 01_[Model]_Testing_Basics.ipynb
├── 02_[Model]_Advanced_Features.ipynb
├── 03_[Model]_Simple_RAG.ipynb
├── 04_[Model]_Advanced_RAG.ipynb
├── 05_[Model]_CrewAI_Agents.ipynb
├── 06_[Model]_Agno_Agents.ipynb
├── 07_[Model]_Multimodal_RAG.ipynb
├── 08_[Model]_LangChain_Complete.ipynb
├── 09_[Model]_LangGraph_Complete.ipynb
├── 10_[Model]_Specialized_UseCases.ipynb
└── README.md

Quick Tips

• Use descriptive variable names (snake_case)
• Keep notebooks under 15min runtime for basics
• Test in clean Colab environment
• Include real-world examples
• Update within 48hrs of new model release
• Share on Twitter with highlights

Maintained by: @BuildFastWithAI