Skill: Reddit SaaS Opportunity Hunter

Production-quality Reddit scraping toolkit for discovering unaddressed user frustrations and emerging market trends for software development.

Overview

This skill provides a comprehensive framework for Reddit-based SaaS opportunity discovery through three distinct layers:

1. Navigation - Where to look (subreddit discovery, wiki scanning, contributor analysis)
2. Extraction - What to pull (high-intent patterns, sentiment scoring, velocity tracking)
3. Synthesis - How to interpret (trend persistence, cross-pollination, opportunity scoring)

Prerequisites

Required Credentials

The skill uses the same credential hierarchy as saas-idea-finder:

1. Environment variables (highest priority)
2. Optional vault/secret store (for service users like openclaw)
3. Shell startup file such as ~/.bashrc (fallback)

Environment variables:

bash
export REDDIT_PROXY_URL="http://user:pass@proxy:8080"  # Residential proxy (recommended)
export REDDIT_CLIENT_ID="your_reddit_app_id"           # OAuth2 (optional, for higher rate limits)
export REDDIT_CLIENT_SECRET="your_reddit_app_secret"   # OAuth2 (optional)

Optional vault/secret store setup:

bash
vault kv put secret/skills-apis/reddit/proxy_url value="http://user:pass@proxy:8080"
vault kv put secret/skills-apis/reddit/client_id value="your_client_id"
vault kv put secret/skills-apis/reddit/client_secret value="your_client_secret"

Rate Limits

• Unauthenticated: ~10 requests/minute (6 second delays)
• OAuth2 Authenticated: 100 requests/minute (0.6 second delays)
• With Residential Proxy: More reliable access, less blocking

Quick Start

Basic Usage

python
import asyncio
from scripts import RedditScraper, OpportunityAnalyzer

async def main():
    # Initialize scraper (auto-loads credentials)
    scraper = RedditScraper()
    
    # Fetch posts from a subreddit
    posts = await scraper.get_subreddit_posts("startups", limit=50)
    
    # Analyze for opportunities
    analyzer = OpportunityAnalyzer()
    for post in posts:
        analysis = analyzer.analyze_post(post)
        if analysis["intent_signals"]:
            print(f"💡 Opportunity: {post.title}")
            print(f"   Problem: {analysis['problem_statement']}")
    
    await scraper.close()

asyncio.run(main())

High-Level Synthesis

python
from scripts import RedditScraper, IdeaSynthesizer

async def main():
    scraper = RedditScraper()
    synthesizer = IdeaSynthesizer(scraper)
    
    # Comprehensive analysis across multiple subreddits
    report = await synthesizer.analyze_opportunity(
        subreddits=["startups", "SaaS", "Entrepreneur"],
        known_competitors=["Salesforce", "HubSpot"],
        min_opportunity_score=60
    )
    
    print(f"Found {report['high_opportunity_count']} high-opportunity signals")
    print(f"Average score: {report['summary']['average_opportunity_score']}")
    
    for opp in report['opportunities'][:5]:
        signal = opp['opportunity']
        print(f"\n🎯 {signal['opportunity_score']}/100 - {signal['priority'].upper()}")
        print(f"   Problem: {signal['problem_statement'][:100]}...")
        print(f"   Action: {signal['recommended_action']}")
    
    await scraper.close()

Tactical Navigation Functions

Find Relevant Subreddits

python
from scripts import RedditScraper, RedditNavigator

scraper = RedditScraper()
navigator = RedditNavigator(scraper)

# Find "watering holes" for your target audience
subreddits = await navigator.get_relevant_subreddits(
    niche_keyword="ecommerce",
    limit=10,
    min_subscribers=5000
)

for sub in subreddits:
    print(f"r/{sub['name']}: {sub['subscribers']:,} subscribers")
    print(f"   Relevance: {sub['relevance_score']:.1f}")
    print(f"   Activity: {sub['activity_ratio']:.3f}")

Scan Community Wiki for Pain Points

python
# Extract FAQs and common questions (literally lists of pain points)
wiki_analysis = await navigator.scan_community_wiki("startups")

print(f"Found {wiki_analysis['pain_point_count']} pain points:")
for pain in wiki_analysis['pain_points'][:10]:
    print(f"  • {pain}")

print(f"\nCommon questions:")
for q in wiki_analysis['common_questions'][:10]:
    print(f"  • {q}")

Identify Top Contributors

python
# Find "power users" who spark high-engagement debates
contributors = await navigator.identify_top_contributors(
    subreddit="startups",
    limit=15,
    activity_lookback=100
)

for c in contributors[:5]:
    print(f"u/{c['username']}: {c['post_count']} posts")
    print(f"   Avg engagement: {c['avg_engagement_per_post']}")
    print(f"   Quality score: {c['quality_score']}")

Recursive Comment Threading

python
# Fetch nested comments looking for "I agree" chains
thread_analysis = await navigator.recursive_comment_threading(
    subreddit="startups",
    post_id="abc123",
    max_depth=5
)

print(f"Total comments: {thread_analysis['total_comments']}")
print(f"Agreement ratio: {thread_analysis['agreement_ratio']:.1%}")
print(f"Pain amplification: {thread_analysis['pain_amplification']}")

# High-intent comments indicate strong demand
for comment in thread_analysis['high_intent_comments'][:5]:
    print(f"  💬 {comment['body'][:100]}...")

Advanced Filtering Capabilities

Intent Pattern Matching

python
from scripts.filters import IntentFilter, filter_high_intent_posts

# Filter for high-intent phrases
intent_filter = IntentFilter()
filtered_posts = intent_filter.filter(posts)

# Or use the convenience function
high_intent = filter_high_intent_posts(posts, min_confidence=0.3)

for post in high_intent:
    matches = post.metadata.get("intent_matches", [])
    print(f"🎯 {post.title}")
    print(f"   Signals: {', '.join(matches)}")

The "Negative Search" (Churn Signals)

python
from scripts.filters import NegativeSearchFilter, find_churn_signals

# Find people quitting products (ready-to-churn users)
churn_signals = find_churn_signals(
    posts,
    target_products=["Salesforce", "HubSpot"]  # Optional: focus on specific products
)

for signal in churn_signals:
    print(f"⚠️  {signal['churn_type'].upper()}: {signal['post'].title}")
    if signal['product']:
        print(f"   Product: {signal['product']}")
    print(f"   Context: {signal['context'][:150]}...")

Velocity Tracking

python
from scripts.filters import VelocityFilter, find_controversial_posts

# Find posts with high engagement velocity
velocity_filter = VelocityFilter(
    min_upvotes_per_hour=1.0,
    min_engagement_ratio=0.1
)
hot_posts = velocity_filter.filter_by_velocity(posts)

# High comment-to-upvote ratio = controversial/painful
controversial = find_controversial_posts(posts, min_ratio=0.3)

for post, ratio in controversial[:5]:
    print(f"🔥 {ratio:.2f} ratio: {post.title}")
    print(f"   {post.comments_count} comments / {post.engagement} upvotes")

Competitor Mention Extraction

python
from scripts.filters import CompetitorFilter

# Track mentions of known competitors
competitor_filter = CompetitorFilter(
    known_competitors=["Salesforce", "HubSpot", "Zapier"]
)

mentions = competitor_filter.extract_mentions(posts)
for brand, mentions_list in mentions.items():
    print(f"📊 {brand}: {len(mentions_list)} mentions")
    for m in mentions_list[:3]:
        print(f"   {m['mention_type']} - {m['intent']}")

# Find feature gaps
gaps = competitor_filter.find_competitor_gaps(posts)
for gap in gaps[:5]:
    print(f"🕳️  Gap: {gap['gap_description']}")
    print(f"   Confidence: {gap['confidence']}")

Idea Synthesis

Trend Persistence Check

python
from scripts import TrendAnalyzer

trend_analyzer = TrendAnalyzer(scraper)

# Check if a problem is a "fad" or "chronic pain point"
report = await trend_analyzer.check_trend_persistence(
    keyword="inventory management",
    subreddits=["smallbusiness", "Etsy", "AmazonFBA"],
    days=90
)

print(f"Keyword: {report.keyword}")
print(f"Is persistent: {report.is_persistent}")
print(f"30-day mentions: {report.mention_count_30d}")
print(f"90-day mentions: {report.mention_count_90d}")
print(f"Growth rate: {report.growth_rate:+.1f}%")
print(f"Prediction: {report.prediction}")

Cross-Pollination Detection

python
from scripts import CrossPollinationAnalyzer

cross_analyzer = CrossPollinationAnalyzer(scraper)

# Find similar problems across different niches
report = await cross_analyzer.find_cross_pollination(
    subreddit_a="EtsySellers",
    subreddit_b="AmazonFBA"
)

print(f"Similarity score: {report.similarity_score:.2f}")
print(f"Opportunity type: {report.opportunity_type}")
print(f"Shared keywords: {', '.join(report.shared_keywords[:10])}")

# Pain points in both communities
for pain in report.pain_points_a[:5]:
    print(f"  A: {pain}")
for pain in report.pain_points_b[:5]:
    print(f"  B: {pain}")

Comprehensive Opportunity Analysis

python
from scripts import IdeaSynthesizer

synthesizer = IdeaSynthesizer(scraper)

# Full synthesis pipeline
report = await synthesizer.analyze_opportunity(
    subreddits=["startups", "SaaS", "Entrepreneur", "marketing"],
    keywords=["automation", "AI", "workflow"],
    known_competitors=["Zapier", "Make", "n8n"],
    min_opportunity_score=65
)

summary = report['summary']
print(f"Priority distribution: {summary['priority_distribution']}")
print(f"Avg opportunity score: {summary['average_opportunity_score']}")
print(f"Avg frustration score: {summary['average_frustration_score']}")

print("\nTop suggested features:")
for feature, count in summary['top_suggested_features'][:5]:
    print(f"  • {feature} ({count} mentions)")

print(f"\nRecommendation: {summary['recommendation']}")

Data Models

ScrapedPost

python
@dataclass
class ScrapedPost:
    id: str
    platform: str
    title: str
    body: str
    url: str
    author: str
    engagement: int           # Upvotes/score
    comments_count: int
    created_at: str
    metadata: Dict[str, Any]  # subreddit, flair, upvote_ratio, etc.
    
    # Computed fields
    pain_score: float = 0.0
    opportunity_score: float = 0.0
    is_high_opportunity: bool = False

OpportunitySignal

python
@dataclass
class OpportunitySignal:
    opportunity_score: float      # 0-100
    priority: str                 # high, medium, low, monitor
    intent_score: float
    frustration_score: float
    velocity_score: float
    trend_score: float
    monetization_score: float
    problem_statement: str
    target_audience: str
    suggested_features: List[str]
    tech_stack_hints: List[str]
    competitor_gaps: List[str]
    recommended_action: str

Testing

Run Tests

bash
# All tests
python3 tests/run_tests.py

# Skip API calls (structural tests only)
python3 tests/run_tests.py --quick

# Analysis tests only
python3 tests/run_tests.py --analysis

# Scraper tests only
python3 tests/run_tests.py --scraper

Quality Thresholds

Tests validate:

• Intent detection: >90% accuracy on test cases
• Frustration scoring: Within expected ranges
• Data quality: >15% pain point detection rate
• API reliability: <10% failure rate

Pro Tips

The "Negative Search"

Don't just search for what people want. Search for "I'm quitting Product]":

python
# This finds ready-to-churn users and tells you what NOT to build
churn_signals = find_churn_signals(posts, target_products=["CompetitorX"])

for signal in churn_signals:
    print(f"Why they're leaving: {signal['context']}")
    # Build the OPPOSITE of these complaints

High Comment-to-Upvote Ratio

Posts with high comments relative to upvotes often indicate:

• Controversial topics
• Deeply felt problems
• Active pain points

python
controversial = find_controversial_posts(posts, min_ratio=0.3)
# These are goldmines for SaaS opportunities

Cross-Niche Validation

If the same problem exists in r/EtsySellers AND r/AmazonFBA:

• It's likely a universal pain point
• Solution can potentially serve both markets
• Higher total addressable market

python
report = await cross_analyzer.find_cross_pollination("EtsySellers", "AmazonFBA")
if report.similarity_score > 0.3:
    print("Cross-niche opportunity detected!")

Error Handling

The scraper implements multiple resilience patterns:

• Circuit Breaker: Prevents cascading failures after 3 consecutive errors
• Exponential Backoff: 2^attempt seconds between retries
• User-Agent Rotation: 7 realistic browser UAs
• Proxy Support: Automatic via credential hierarchy
• Shadowban Detection: Detects and works around blocks
• Old Reddit Fallback: Falls back to old.reddit.com on 403s

Troubleshooting

"No proxy configured" Warning

Reddit may block cloud/datacenter IPs. Set up a residential proxy:

bash
export REDDIT_PROXY_URL="http://user:pass@residential.proxy:8080"

Rate Limiting (429 Errors)

The scraper automatically handles 429s with exponential backoff. To reduce frequency:

• Use OAuth2 credentials (100 req/min vs 10 req/min)
• Use a residential proxy
• Increase min_delay in scraper initialization

Empty Results

If you get empty results:

• Check if OAuth2 is configured (some endpoints require auth)
• Try different subreddits (some block API access)
• Check the subreddit actually exists and is public

Architecture

skills-global/scraping-reddit/
├── scripts/
│   ├── reddit_scraper.py    # Core scraper with robustness patterns
│   ├── analysis.py          # Pain point & opportunity analysis
│   ├── navigation.py        # Subreddit/wiki discovery
│   ├── synthesis.py         # Trend & cross-pollination analysis
│   ├── filters.py           # Semantic filtering
│   ├── credentials.py       # Credential loading
│   └── utils.py             # Dataclasses & utilities
├── tests/
│   ├── test_scraper.py      # Scraper tests
│   ├── test_analysis.py     # Algorithm tests
│   └── run_tests.py         # Test runner
└── SKILL.md                 # This file

References

• Reddit JSON API endpoints documented in REFERENCES.md
• Credential loading follows saas-idea-finder pattern
• Rate limits: 10 req/min (unauth), 100 req/min (OAuth)

License

This skill follows the same patterns and robustness standards as saas-idea-finder.