How to Create an AI Social Media Content Repurposer

I built this system to transform blog posts, podcasts, and videos into platform-native social content. This ai social media content repurposing tool setup automates the repetitive work so you can focus on strategy.

Social media at scale needs systems. Manual posting and tracking breaks past a few accounts.

What You Need Before Starting

• Python 3.8+ with requests
• Anthropic API key for content generation
• Platform API credentials
• SQLite for tracking

Step 1: Set Up Data Storage

import sqlite3
from datetime import datetime

db = sqlite3.connect("social_system.db")
db.execute('''CREATE TABLE IF NOT EXISTS social_data (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    platform TEXT, content TEXT, engagement INTEGER,
    created_at TEXT, status TEXT
)''')
db.commit()

Step 2: Build the AI Engine

import anthropic
from dotenv import load_dotenv

load_dotenv()
client = anthropic.Anthropic()

def generate_content(platform, topic, voice):
    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        system=f"Create {platform}-native content. Voice: {voice}. Drive engagement.",
        messages=[{"role": "user", "content": f"Platform: {platform}. Topic: {topic}. Generate post."}]
    )
    return message.content[0].text

Step 3: Connect to Platform APIs

import requests
import os

def post_to_platform(platform, content):
    if platform == "linkedin":
        return requests.post(
            "https://api.linkedin.com/v2/ugcPosts",
            headers={"Authorization": f"Bearer {os.getenv('LINKEDIN_TOKEN')}"},
            json={"content": content}
        )
    elif platform == "facebook":
        return requests.post(
            f"https://graph.facebook.com/v18.0/me/feed",
            params={"access_token": os.getenv("META_TOKEN"), "message": content}
        )

Step 4: Track Performance

def log_performance(platform, post_id, metrics):
    db.execute("INSERT INTO social_data (platform, content, engagement, created_at, status) VALUES (?,?,?,?,?)",
        (platform, post_id, metrics.get("engagement", 0), datetime.now().isoformat(), "tracked"))
    db.commit()

# Pull metrics daily
0 8 * * * cd /app && python pull_social_metrics.py

What to Build Next

Add AI analysis that identifies your top content patterns and generates more of what performs best.

Related Reading

• AI for Social Media Management - AI-powered social media management
• How to Create Social Proof Content with AI - scaling content production with AI systems
• Building a Content Repurposing Workflow - scaling content production with AI systems