🚀 Getting Started

Run your first VOID Loop workflow in 10 minutes

In this guide: Prerequisites • Installation • First Workflow • Next Steps

Prerequisites

What you'll need

  • Python 3.8+ installed on your system
  • Basic SEO knowledge (keywords, SERP features)
  • OpenAI API key (for AI-powered clustering)
  • 10 minutes of focused time

🐍 Check Python Version

python --version
# Should show Python 3.8 or higher

🔑 Get OpenAI API Key

Visit OpenAI Platform to create your API key.

Installation

1

Clone the Repository

# Clone VoidSEO tools
git clone https://github.com/voidseo/tools.git
cd voidseo-tools
2

Install Dependencies

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install requirements
pip install -r requirements.txt
3

Configure Environment

# Copy environment template
cp .env.example .env

# Edit .env file with your API keys
OPENAI_API_KEY=your_openai_key_here
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_key

Your First VOID Loop Workflow

🎯 What we'll build

A PAA (People Also Ask) analysis workflow that identifies content gaps in your niche using the complete VOID Loop methodology.

V

Vision

Pattern: "I need to find content gaps by analyzing what questions people ask about my topic."

Why automate: Manual PAA research is time-consuming and inconsistent.

O

Objective

Input: 5-10 seed keywords in your niche

Output: Clustered PAA questions with content opportunity scores

Success metric: Find 20+ unique content ideas in under 5 minutes

I

Implementation

Let's build it step by step:

Step 1: Import and Initialize
from voidseo import PAAExplorer
import json

# Initialize the PAA Explorer
explorer = PAAExplorer(
    openai_api_key="your_key_here",
    max_questions_per_keyword=10
)
Step 2: Define Your Keywords
# Your seed keywords (adjust for your niche)
keywords = [
    "seo tools",
    "keyword research", 
    "content optimization",
    "serp analysis",
    "backlink analysis"
]
Step 3: Scrape PAA Questions
# Scrape PAA questions for each keyword
print("🔍 Scraping PAA questions...")
questions = explorer.scrape_paa_questions(keywords)
print(f"Found {len(questions)} questions")
Step 4: Cluster by Intent
# Cluster questions by semantic similarity
print("🧠 Clustering questions by intent...")
clusters = explorer.cluster_questions(
    questions, 
    n_clusters="auto",
    algorithm="kmeans"
)
print(f"Created {len(clusters)} content clusters")
Step 5: Export Results
# Export to CSV for analysis
explorer.export_csv(clusters, "paa_content_gaps.csv")
print("✅ Results exported to paa_content_gaps.csv")
D

Deep Dive

Analyze your results:

  • Which clusters have the most questions? (High demand topics)
  • Which clusters are underserved by existing content?
  • What's the search intent behind each cluster?
💡 Pro Tip

Look for clusters with 5+ questions but low competition. These are your golden content opportunities!

Next Steps

📚 Learn the Framework

Understand the complete VOID Loop methodology and how to apply it to any SEO workflow.

Read Framework Guide

🛠️ Explore More Apps

Try our AI Overview Detector and other tools built with the same methodology.

Browse Apps

🔧 API Integration

Integrate VoidSEO tools into your existing workflows with our REST API.

API Reference

💬 Join Community

Connect with other builders, share your workflows, and get help.

Join Discord