Imported from reason-machines/marketing-skills (skills/reddit-marketing-agent/SKILL.md) via skills.sh. Install upstream with npx skills add reason-machines/marketing-skills --skill reddit-marketing-agent. Copyright stays with the author.

Reddit Marketing Agent

Skill by ara.so — Marketing Skills collection.

This skill enables AI coding agents to help developers use the Reddit Marketing Agent, a system for researching relevant Reddit threads, generating useful responses, and turning repeatable Reddit workflows into scalable growth operations. The agent identifies high-intent conversations across subreddits, drafts context-aware responses, and supports repeatable workflows for research and optimization.

What It Does

The Reddit Marketing Agent helps you:

Identify high-intent conversations and opportunities across relevant subreddits
Draft context-aware, non-spammy responses and content angles
Support repeatable workflows for research, posting support, and iteration
Keep strategy, logs, and source context organized for ongoing optimization
Scale Reddit community engagement without appearing spammy

Built by the AI Automation Mastery community for systematic Reddit growth.

Installation

Clone the repository:

git clone https://github.com/lucaswalter/reddit-marketing-agent.git
cd reddit-marketing-agent

Install dependencies (typically Python-based):

pip install -r requirements.txt
# or
pip install praw openai python-dotenv

Set up environment variables in .env:

REDDIT_CLIENT_ID=your_reddit_client_id
REDDIT_CLIENT_SECRET=your_reddit_client_secret
REDDIT_USER_AGENT=your_user_agent
OPENAI_API_KEY=your_openai_api_key

Configuration

Create a config.yaml or config.json file to define your target subreddits and search parameters:

subreddits:
  - "Entrepreneur"
  - "startups"
  - "SaaS"
  - "marketing"

keywords:
  - "need automation"
  - "looking for tools"
  - "recommend software"
  - "help with marketing"

filters:
  min_upvotes: 5
  max_age_hours: 48
  exclude_patterns:
    - "spam"
    - "promotion"

response_settings:
  tone: "helpful"
  max_length: 300
  include_value_first: true

Core Usage Patterns

1. Search for Relevant Threads

import praw
from dotenv import load_dotenv
import os

load_dotenv()

# Initialize Reddit API client
reddit = praw.Reddit(
    client_id=os.getenv('REDDIT_CLIENT_ID'),
    client_secret=os.getenv('REDDIT_CLIENT_SECRET'),
    user_agent=os.getenv('REDDIT_USER_AGENT')
)

def search_relevant_threads(subreddit_name, keywords, limit=25):
    """Search for threads matching keywords in a subreddit."""
    subreddit = reddit.subreddit(subreddit_name)
    relevant_threads = []
    
    for keyword in keywords:
        for submission in subreddit.search(keyword, time_filter='week', limit=limit):
            if submission.score >= 5:  # Filter by upvotes
                relevant_threads.append({
                    'title': submission.title,
                    'url': submission.url,
                    'score': submission.score,
                    'num_comments': submission.num_comments,
                    'created_utc': submission.created_utc,
                    'id': submission.id,
                    'selftext': submission.selftext
                })
    
    return relevant_threads

# Example usage
threads = search_relevant_threads('Entrepreneur', ['automation tools', 'marketing help'], limit=10)
for thread in threads:
    print(f"{thread['title']} - Score: {thread['score']}")

2. Generate Context-Aware Responses

import openai
import os

openai.api_key = os.getenv('OPENAI_API_KEY')

def generate_response(thread_title, thread_content, tone='helpful'):
    """Generate a contextual, non-spammy Reddit response."""
    
    prompt = f"""You are a helpful community member on Reddit. Generate a genuine, value-first response to this thread.

Thread Title: {thread_title}
Thread Content: {thread_content}

Guidelines:
- Be genuinely helpful and specific
- Provide value before any mentions
- Keep tone {tone} and conversational
- Avoid obvious promotion
- Max 250 words

Response:"""

    response = openai.ChatCompletion.create(
        model='gpt-4',
        messages=[
            {'role': 'system', 'content': 'You are an experienced marketer who engages authentically on Reddit.'},
            {'role': 'user', 'content': prompt}
        ],
        temperature=0.7,
        max_tokens=400
    )
    
    return response.choices[0].message.content

# Example usage
response = generate_response(
    "Need help automating my social media",
    "I'm spending 3 hours a day on social media for my startup. Any tools or strategies?"
)
print(response)

3. Complete Workflow Script

import json
from datetime import datetime

def reddit_marketing_workflow(config_path='config.json'):
    """Complete workflow: search, analyze, generate responses, log results."""
    
    # Load configuration
    with open(config_path, 'r') as f:
        config = json.load(f)
    
    results = []
    
    for subreddit_name in config['subreddits']:
        print(f"\n🔍 Searching r/{subreddit_name}...")
        
        # Search threads
        threads = search_relevant_threads(
            subreddit_name, 
            config['keywords'],
            limit=config.get('limit', 25)
        )
        
        # Filter by criteria
        filtered_threads = [
            t for t in threads 
            if t['score'] >= config['filters']['min_upvotes']
        ]
        
        print(f"✅ Found {len(filtered_threads)} relevant threads")
        
        # Generate responses for top threads
        for thread in filtered_threads[:5]:  # Top 5
            response = generate_response(
                thread['title'],
                thread['selftext'],
                tone=config['response_settings']['tone']
            )
            
            results.append({
                'subreddit': subreddit_name,
                'thread_id': thread['id'],
                'thread_title': thread['title'],
                'thread_url': thread['url'],
                'score': thread['score'],
                'generated_response': response,
                'timestamp': datetime.now().isoformat()
            })
    
    # Save results
    output_file = f"reddit_opportunities_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
    with open(output_file, 'w') as f:
        json.dump(results, f, indent=2)
    
    print(f"\n💾 Results saved to {output_file}")
    return results

# Run workflow
results = reddit_marketing_workflow()

4. Sentiment and Opportunity Scoring

def score_opportunity(thread):
    """Score a thread's marketing opportunity potential."""
    score = 0
    
    # Engagement metrics
    if thread['score'] > 20:
        score += 2
    if thread['num_comments'] > 10:
        score += 2
    
    # Intent signals in title/content
    high_intent_phrases = ['recommend', 'looking for', 'need help', 'suggestions', 'alternatives']
    text = (thread['title'] + ' ' + thread['selftext']).lower()
    
    for phrase in high_intent_phrases:
        if phrase in text:
            score += 3
    
    # Recency (threads less than 24 hours old)
    age_hours = (datetime.now().timestamp() - thread['created_utc']) / 3600
    if age_hours < 24:
        score += 2
    
    return score

# Example usage
threads = search_relevant_threads('SaaS', ['automation'], limit=20)
scored_threads = sorted(
    [(score_opportunity(t), t) for t in threads],
    key=lambda x: x[0],
    reverse=True
)

print("Top opportunities:")
for score, thread in scored_threads[:5]:
    print(f"Score {score}: {thread['title']}")

Command Line Interface

If the project includes a CLI script:

# Search for opportunities
python reddit_agent.py search --subreddits "Entrepreneur,startups" --keywords "automation,tools"

# Generate responses for saved threads
python reddit_agent.py generate --input threads.json --output responses.json

# Run full workflow
python reddit_agent.py workflow --config config.yaml

# Analyze subreddit activity
python reddit_agent.py analyze --subreddit SaaS --days 7

Data Storage and Logging

Keep track of opportunities and responses:

import sqlite3

def setup_database():
    """Create SQLite database for tracking threads and responses."""
    conn = sqlite3.connect('reddit_marketing.db')
    c = conn.cursor()
    
    c.execute('''CREATE TABLE IF NOT EXISTS threads
                 (id TEXT PRIMARY KEY,
                  subreddit TEXT,
                  title TEXT,
                  url TEXT,
                  score INTEGER,
                  opportunity_score INTEGER,
                  found_date TEXT,
                  status TEXT)''')
    
    c.execute('''CREATE TABLE IF NOT EXISTS responses
                 (id INTEGER PRIMARY KEY AUTOINCREMENT,
                  thread_id TEXT,
                  generated_response TEXT,
                  posted BOOLEAN,
                  posted_date TEXT,
                  FOREIGN KEY(thread_id) REFERENCES threads(id))''')
    
    conn.commit()
    conn.close()

def log_thread(thread_data):
    """Log a discovered thread to database."""
    conn = sqlite3.connect('reddit_marketing.db')
    c = conn.cursor()
    
    c.execute('''INSERT OR IGNORE INTO threads 
                 (id, subreddit, title, url, score, opportunity_score, found_date, status)
                 VALUES (?, ?, ?, ?, ?, ?, ?, ?)''',
              (thread_data['id'], thread_data['subreddit'], thread_data['title'],
               thread_data['url'], thread_data['score'], thread_data['opportunity_score'],
               datetime.now().isoformat(), 'new'))
    
    conn.commit()
    conn.close()

Best Practices

Non-Spammy Engagement

ENGAGEMENT_RULES = {
    'value_first': True,
    'max_daily_comments': 5,
    'wait_between_comments_minutes': 60,
    'personalize_each_response': True,
    'avoid_direct_promotion': True
}

def is_safe_to_engage(thread):
    """Check if engagement is appropriate."""
    # Don't engage if already commented
    # Check rate limits
    # Verify thread age and activity
    # Ensure genuine opportunity
    return True  # Implement your logic

Troubleshooting

Reddit API Rate Limits:

PRAW handles most rate limiting automatically
Add delays between requests: time.sleep(2)
Use reddit.auth.limits to check remaining requests

Authentication Errors:

Verify .env file contains correct credentials
Ensure Reddit app is configured as "script" type
Check that user agent string is descriptive and unique

Empty Search Results:

Broaden keyword search terms
Increase time filter (e.g., 'month' instead of 'week')
Verify subreddit names are correct
Check if subreddits are private or restricted

OpenAI API Errors:

Verify OPENAI_API_KEY is set correctly
Check API quota and billing status
Reduce max_tokens if hitting limits
Add retry logic with exponential backoff

Advanced Features

Multi-Subreddit Monitoring

import schedule
import time

def monitor_subreddits():
    """Continuously monitor subreddits for new opportunities."""
    config = load_config('config.json')
    results = reddit_marketing_workflow(config)
    print(f"Found {len(results)} new opportunities")

# Schedule monitoring
schedule.every(2).hours.do(monitor_subreddits)

while True:
    schedule.run_pending()
    time.sleep(60)

Response Quality Validation

def validate_response(response_text):
    """Ensure generated response meets quality standards."""
    checks = {
        'not_too_short': len(response_text) > 50,
        'not_too_long': len(response_text) < 500,
        'no_spam_words': not any(word in response_text.lower() for word in ['buy now', 'click here', 'limited offer']),
        'has_value': any(word in response_text.lower() for word in ['try', 'suggest', 'recommend', 'help', 'consider'])
    }
    
    return all(checks.values())

This skill provides comprehensive guidance for using the Reddit Marketing Agent to identify opportunities, generate contextual responses, and scale Reddit community engagement systematically.

reddit-marketing-agent

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