This Copilot Prompt Produces SEO-Ranked Blog Posts—And It’s Free

# script_01_research.py
import requests
import json
import pandas as pd
from pytrends.request import TrendReq # pip install pytrends
from dotenv import load_dotenv # pip install python-dotenv
import os

load_dotenv() # Load environment variables from .env file

SERPAPI_KEY = os.getenv("SERPAPI_KEY") # Get your SerpAPI key from .env

def get_serp_results(query, api_key):
    """Fetches top 10 organic search results for a given query using SerpAPI."""
    url = "https://serpapi.com/search"
    params = {
        "api_key": api_key,
        "engine": "google",
        "q": query,
        "gl": "us", # Google location
        "hl": "en"  # Host language
    }
    response = requests.get(url, params=params)
    response.raise_for_status() # Raise an exception for HTTP errors
    search_results = response.json()
    
    competitors = []
    if "organic_results" in search_results:
        for result in search_results["organic_results"][:5]: # Top 5 competitors
            competitors.append({
                "title": result.get("title"),
                "link": result.get("link"),
                "snippet": result.get("snippet")
            })
    return competitors

def get_related_queries(keyword):
    """Fetches related queries/topics using Google Trends (via pytrends)."""
    pytrends = TrendReq(hl='en-US', tz=360)
    pytrends.build_payload([keyword], cat=0, timeframe='today 12-m', geo='')
    related_queries = pytrends.related_queries()
    
    secondary_keywords = []
    if keyword in related_queries and 'top' in related_queries[keyword]:
        # Extract keywords and filter for relevance, perhaps based on 'value'
        df = related_queries[keyword]['top']
        # Simple heuristic: take top 5-10 related queries as secondary keywords
        secondary_keywords = df['query'].head(10).tolist()
    return secondary_keywords

if __name__ == "__main__":
    primary_keyword = input("Enter the primary keyword: ")
    
    print(f"\n--- Analyzing SERP for '{primary_keyword}' ---")
    competitor_data = get_serp_results(primary_keyword, SERPAPI_KEY)
    print("Top Competitors:")
    competitor_urls_titles = []
    for i, comp in enumerate(competitor_data):
        print(f"{i+1}. Title: {comp['title']}\n   URL: {comp['link']}")
        competitor_urls_titles.append(f"{comp['title']} ({comp['link']})")

    print(f"\n--- Fetching Related Queries for '{primary_keyword}' ---")
    lsi_keywords = get_related_queries(primary_keyword)
    print("Suggested Secondary Keywords (LSI):")
    for kw in lsi_keywords:
        print(f"- {kw}")
        
    # Save for later use in prompt
    with open("research_data.json", "w") as f:
        json.dump({
            "primary_keyword": primary_keyword,
            "competitors": competitor_urls_titles,
            "secondary_keywords": lsi_keywords
        }, f, indent=4)
    print("\nResearch data saved to research_data.json")

# script_02_generate_content.py
import openai
import json
import os
from dotenv import load_dotenv

load_dotenv()

# Configure OpenAI API key
# Ensure you have OPENAI_API_KEY set in your .env file
openai.api_key = os.getenv("OPENAI_API_KEY")

def generate_blog_post(prompt_template, research_data, custom_inputs):
    """Generates a blog post using the GPT-5 API based on the structured prompt."""
    
    # Extract data from research_data
    primary_keyword = research_data["primary_keyword"]
    competitors_str = "\n".join(research_data["competitors"])
    secondary_keywords_csv = ", ".join(research_data["secondary_keywords"])

    # Prepare custom inputs (can be hardcoded or dynamically pulled from a config)
    target_audience = custom_inputs.get("target_audience", "solo content creators, Python developers, new parents")
    user_intent = custom_inputs.get("user_intent", "informational, seeking tools and strategies")
    desired_tone = custom_inputs.get("desired_tone", "authoritative, friendly, educational, witty")
    cta_goal = custom_inputs.get("cta_goal", "sign up for newsletter")
    internal_links = custom_inputs.get("internal_links", "none") 
    external_links = custom_inputs.get("external_links", "none") 


    # Fill the prompt template
    filled_prompt = prompt_template.replace("[INSERT_PRIMARY_KEYWORD]", primary_keyword) \
                                 .replace("[INSERT_TARGET_AUDIENCE]", target_audience) \
                                 .replace("[INSERT_USER_INTENT]", user_intent) \
                                 .replace("[INSERT_COMPETITOR_URLS_AND_TITLES_FOR_REFERENCE]", competitors_str) \
                                 .replace("[INSERT_SECONDARY_KEYWORDS_CSV]", secondary_keywords_csv) \
                                 .replace("[INSERT_TONE]", desired_tone) \
                                 .replace("[INSERT_INTERNAL_LINK_ANCHOR_TEXT_AND_URLS_CSV_OR_NONE]", internal_links) \
                                 .replace("[INSERT_EXTERNAL_LINK_TOPICS_OR_NONE]", external_links) \
                                 .replace("[INSERT_CTA_GOAL]", cta_goal)

    messages = [
        {"role": "system", "content": "You are an expert SEO content strategist and writer. Your goal is to produce a comprehensive, high-ranking blog post."},
        {"role": "user", "content": filled_prompt}
    ]

    try:
        response = openai.chat.completions.create(
            model="gpt-5", # Assuming GPT-5 is available in 2025
            messages=messages,
            temperature=0.7, # Adjust for creativity vs. consistency
            max_tokens=3000, # Max tokens for the output
            top_p=1
        )
        return response.choices[0].message.content
    except openai.APIError as e:
        print(f"OpenAI API Error: {e}")
        return None

if __name__ == "__main__":
    # Load research data
    try:
        with open("research_data.json", "r") as f:
            research_data = json.load(f)
    except FileNotFoundError:
        print("Error: research_data.json not found. Run script_01_research.py first.")
        exit()

    # Define the base prompt (same as the one explained above)
    copilot_prompt_template = """
You are an expert SEO content strategist and writer. Your goal is to produce a comprehensive, 1500-2000 word blog post that ranks highly for the primary keyword, satisfies user intent, and includes secondary LSI keywords.

**Input Data:**
- Primary Keyword: [INSERT_PRIMARY_KEYWORD]
- Target Audience: [INSERT_TARGET_AUDIENCE]
- User Intent: [INSERT_USER_INTENT]
- Key Competitors (from SERP analysis): [INSERT_COMPETITOR_URLS_AND_TITLES_FOR_REFERENCE]
- Secondary LSI Keywords: [INSERT_SECONDARY_KEYWORDS_CSV]
- Desired Tone: [INSERT_TONE]
- Internal Link Suggestions: [INSERT_INTERNAL_LINK_ANCHOR_TEXT_AND_URLS_CSV_OR_NONE]
- External Link Suggestions: [INSERT_EXTERNAL_LINK_TOPICS_OR_NONE]
- Call to Action Goal: [INSERT_CTA_GOAL]

**Output Structure & Requirements:**

1.  **Catchy, SEO-Optimized Title (max 60 chars):** Include the primary keyword naturally.
2.  **Meta Description (max 160 chars):** Compelling, includes primary keyword, encourages click-through.
3.  **Introduction:** Hook the reader, clearly state the problem/topic, and outline what the post will cover.
4.  **Main Body (2-4 H2 sections, each with 2-3 H3 subsections):**
    *   Each section must thoroughly cover a sub-topic related to the primary keyword.
    *   Integrate secondary LSI keywords naturally throughout.
    *   Provide actionable insights, examples, or data where relevant.
    *   Ensure smooth transitions between paragraphs and sections.
    *   Incorporate internal links using provided anchor text.
    *   Suggest suitable places for external links to authoritative sources.
5.  **Conclusion:** Summarize key takeaways, reiterate the main message, and include the Call to Action.
6.  **FAQ Section (3-5 questions):** Address common queries related to the topic, incorporating long-tail keywords.
7.  **Readability:** Maintain a conversational yet informative style. Use short paragraphs, bullet points, and numbered lists for easy consumption. Avoid jargon where possible, or explain it clearly.
8.  **Originality:** Ensure the content is unique and doesn't plagiarize existing material.
9.  **SEO Optimization Check:** Verify primary keyword density (1-2%), natural flow, and LSI keyword integration.

**Start generating the blog post now, beginning with the title and meta description.**
"""
    
    # You can customize these per post or load from a config
    custom_post_inputs = {
        "target_audience": "solo content creators, bloggers, and developers seeking efficiency",
        "user_intent": "informational, seeking practical tools and strategies for content automation",
        "desired_tone": "clear, energetic, smart, and highly practical",
        "cta_goal": "encourage readers to download a free 'Content Automation Checklist' and subscribe to our newsletter",
        "internal_links": "mastering AI prompts (https://yourblog.com/master-ai-prompts), scaling content with automation (https://yourblog.com/scale-content), effective blog monetization strategies (https://yourblog.com/monetize-blog)",
        "external_links": "latest industry reports on AI adoption in marketing, academic papers on natural language processing advancements"
    }

    print(f"\n--- Generating content for '{research_data['primary_keyword']}' using GPT-5 ---")
    blog_post_content = generate_blog_post(copilot_prompt_template, research_data, custom_post_inputs)

    if blog_post_content:
        # Extract title and meta description
        lines = blog_post_content.split('\n')
        title = ""
        meta_description = ""
        content_start_index = 0

        # Simple parsing for title and meta assuming they are at the very start
        for i, line in enumerate(lines):
            if line.strip().lower().startswith("title:"):
                title = line.split(":", 1)[1].strip()
            elif line.strip().lower().startswith("meta description:"):
                meta_description = line.split(":", 1)[1].strip()
            elif not line.strip() and title and meta_description: # First empty line after both found
                content_start_index = i + 1
                break
            elif not line.strip() and not (title or meta_description): # If no title/meta, start from first non-empty
                content_start_index = i
                break
        
        # If title/meta not explicitly parsed, take first H1 as title, and first few sentences as meta guess
        if not title:
            h1_match = next((line for line in lines if line.strip().startswith("# ")), None)
            if h1_match:
                title = h1_match.replace("# ", "").strip()
                content_start_index = lines.index(h1_match) + 1 # Start content after H1
            else:
                title = "Generated Blog Post" # Fallback title

        if not meta_description and content_start_index < len(lines):
            # Take first few non-empty lines as meta description guess
            temp_meta_lines = []
            for i in range(content_start_index, min(content_start_index + 5, len(lines))):
                if lines[i].strip():
                    temp_meta_lines.append(lines[i].strip())
                    if len(" ".join(temp_meta_lines)) >= 100: # Stop after reasonable length
                        break
            meta_description = " ".join(temp_meta_lines)[:160].strip() + "..."
        
        main_content = "\n".join(lines[content_start_index:]).strip()

        # Save the generated content
        output_filename = f"blog_post_{primary_keyword.replace(' ', '_').lower()}.md"
        with open(output_filename, "w") as f:
            f.write(f"Title: {title}\n")
            f.write(f"Meta Description: {meta_description}\n\n")
            f.write(main_content)
        print(f"\nBlog post saved to {output_filename}")
        
        # Save structured for publishing later
        post_data = {
            "title": title,
            "meta_description": meta_description,
            "content": main_content,
            "primary_keyword": primary_keyword # for tags/categories
        }
        with open("post_to_publish.json", "w") as f:
            json.dump(post_data, f, indent=4)
        print("Post data prepared for publishing.")
    else:
        print("Failed to generate blog post.")

Title: AI for Content Creation: Your Ultimate 2025 Automation Guide
Meta Description: Unlock peak efficiency in 2025 with AI for content creation. This guide covers tools, strategies, and workflows for solo creators, bloggers, and developers.

# AI for Content Creation: Your Ultimate 2025 Automation Guide

In the rapidly evolving landscape of 2025, solo content creators, bloggers, and developers face an ever-increasing demand for high-quality, consistent content. The good news? Artificial Intelligence (AI) isn't just a buzzword; it's a powerful ally transforming how we research, write, and optimize our digital presence. This guide will walk you through leveraging AI for content creation, from initial ideation to automated publishing, ensuring your efforts are both efficient and impactful.

The goal isn't to replace human creativity, but to augment it, allowing you to scale your output, maintain quality, and free up precious time for strategy and engagement.

## The AI Content Revolution: What's Changed in 2025?

The advancements in Large Language Models (LLMs) like GPT-5 have moved beyond basic article spinning. Today, AI can understand nuanced prompts, generate coherent narratives, and even suggest SEO improvements based on real-time data. This shift empowers creators to produce not just *more* content, but *better*, more targeted content.

### From Manual Labor to Intelligent Automation
Historically, content creation was a highly manual process: hours spent on keyword research, days on drafting, and endless tweaking for SEO. Now, intelligent automation can handle these tasks with remarkable speed and accuracy. This means you can launch new content series faster, explore niche topics more readily, and truly own your content calendar.

### GPT-5 and Beyond: The New Frontier
GPT-5, the latest iteration of OpenAI’s powerful model, offers unprecedented capabilities in understanding context, generating long-form content, and even adapting to specific writing styles. Its ability to process complex instructions from detailed prompts makes it an indispensable tool for serious content creators.

## Setting Up Your AI-Powered Content Workflow

Building an automated content pipeline involves a few key stages: research, generation, optimization, and publishing. Each stage can be enhanced significantly with AI.

### Stage 1: Intelligent Keyword and SERP Analysis
Before writing a single word, understanding what your audience is searching for, and how competitors are addressing those needs, is paramount. AI tools and APIs can accelerate this.

#### Leveraging Google Trends and SERP APIs
Tools like SerpAPI (as demonstrated in `script_01_research.py`) provide programmatic access to real-time search engine results. This allows you to automatically gather competitor titles, descriptions, and structures, giving your AI a strong foundation for understanding search intent. [Suggest external link to academic studies on NLP]

For trending topics and related long-tail keywords, while Google Trends doesn't offer a direct public API, libraries like `pytrends` can scrape its data, providing invaluable insights into what's gaining traction. This helps ensure your content is relevant and timely.

#### Identifying Secondary and LSI Keywords
Beyond the primary keyword, LSI (Latent Semantic Indexing) keywords are crucial for semantic SEO. AI can assist in identifying these related terms by analyzing broad topics and competitor content. Our initial research script does a great job of this using `pytrends` related queries.

### Stage 2: Crafting Content with GPT-5 and Advanced Prompts
This is where our meticulously designed prompt comes into play. By feeding the AI detailed instructions, it can generate high-quality drafts.

#### The Art of Prompt Engineering
The prompt isn't just a question; it's a blueprint. As seen in `script_02_generate_content.py`, the prompt outlines the desired structure, tone, and SEO requirements. It ensures the AI doesn't just write, but writes *strategically*. [Internal link: mastering AI prompts]

#### Integrating Internal and External Links
For SEO, relevant internal and external links are vital. Our prompt includes placeholders for these, allowing you to feed in specific URLs or topics. The AI will then naturally weave these into the narrative, enhancing topical authority and user experience. [Suggest internal link to scaling content with automation]

## Monetizing Your Automated Content Stream

Generating content is one thing; making money from it is another. With an automated system producing high-quality, SEO-optimized articles, your monetization opportunities multiply.

### Ad Revenue
As your traffic grows from consistently ranked content, ad networks like Mediavine, Ezoic, or Google AdSense become viable income streams. More high-quality posts mean more pages indexed, more organic traffic, and thus, more ad impressions.

### Affiliate Marketing
Integrate relevant affiliate links (e.g., Amazon Associates, SaaS tools, digital products) into your AI-generated content. The system helps you produce the sheer volume of content needed to capture long-tail search traffic, which often converts well for affiliate offers. This is a workflow that consistently generates mid to high three-figures, and often much more, per month from relevant niches.

### Digital Products & Services
Use your automated content as a lead magnet. Each blog post can include a Call to Action (CTA) for your own eBooks, online courses, or consulting services. The consistent flow of new content keeps your audience engaged and provides multiple entry points for your offerings.

## Step 3: Automated Publishing to WordPress

Once the content is generated, manual copy-pasting is a bottleneck. Automate publishing using WordPress's XML-RPC API.

```python
# script_03_publish_wordpress.py
from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost
from wordpress_xmlrpc.methods.media import UploadFile # Not used directly in this snippet, but common for media
import os
import json
from dotenv import load_dotenv

load_dotenv()

# WordPress credentials (store securely in .env)
WP_URL = os.getenv("WP_URL") # e.g., 'https://yourdomain.com/xmlrpc.php'
WP_USERNAME = os.getenv("WP_USERNAME")
WP_PASSWORD = os.getenv("WP_PASSWORD")

def publish_post(title, content, primary_keyword, meta_description, status='publish', categories=None, tags=None):
    """Publishes a new post to WordPress."""
    try:
        client = Client(WP_URL, WP_USERNAME, WP_PASSWORD)
        
        post = WordPressPost()
        post.title = title
        post.content = content
        post.post_status = status
        
        # Categories and Tags
        if categories:
            post.categories = categories
        else: # Default category if none provided
            post.categories = ['AI Blogging', 'Automation'] 

        if tags:
            post.tags = tags
        else: # Default tags based on primary keyword
            post.tags = [primary_keyword.replace(' ', '-').lower(), 'AI', 'Automation', 'Content']

        # Custom fields for SEO plugins (e.g., Yoast SEO, Rank Math)
        # You might need to adjust these based on your specific SEO plugin and its XML-RPC integration
        post.custom_fields = []
        if meta_description:
            # Example for Yoast SEO
            post.custom_fields.append({'key': '_yoast_wpseo_metadesc', 'value': meta_description})
            # For Rank Math: post.custom_fields.append({'key': 'rank_math_description', 'value': meta_description})
            
        print(f"Attempting to publish post: {post.title}")
        post_id = client.call(NewPost(post))
        print(f"Successfully published post with ID: {post_id}")
        return post_id
    except Exception as e:
        print(f"Error publishing to WordPress: {e}")
        return None

if __name__ == "__main__":
    try:
        with open("post_to_publish.json", "r") as f:
            post_data = json.load(f)
    except FileNotFoundError:
        print("Error: post_to_publish.json not found. Run script_02_generate_content.py first.")
        exit()

    title = post_data["title"]
    content = post_data["content"]
    primary_keyword = post_data["primary_keyword"]
    meta_description = post_data.get("meta_description", "")
    
    # Customize categories and tags as needed
    post_categories = ['AI Content Automation', 'Content Monetization'] 
    post_tags = [primary_keyword.lower(), 'GPT-5', 'Copilot', 'Automation 2025'] 
    
    post_id = publish_post(title, content, primary_keyword, meta_description, 
                           categories=post_categories, tags=post_tags)

    if post_id:
        print(f"Post successfully published to WordPress with ID: {post_id}")
    else:
        print("Failed to publish post.")