Twitter Creator Insights

This skill provides Twitter/X content creators with actionable intelligence about their account performance, trending topics in their niche, and competitive analysis. Includes account analytics, viral content discovery, thread/follower intelligence, and AI-powered content generation.

When to Use This Skill

Invoke this skill when:

A creator requests analysis of their Twitter account or another account
User asks about trending content or viral tweets in a specific niche
User wants to understand what content performs well in their space
User needs recommendations for improving their Twitter strategy
User asks about competitor or similar account activity
User wants to find influential accounts in a niche
User wants to identify VIP followers or "hidden gem" accounts (NEW)
User asks which threads attracted high-value engagement (NEW)
User needs help drafting tweets or analyzing viral patterns with AI (NEW)
User wants to optimize an existing tweet before posting (NEW)

Core Workflow

The skill follows a fetch → analyze → score → recommend pipeline:

1. Account Analysis Phase

Objective: Deep-dive into a Twitter account's performance and content patterns.

Process:

Run python scripts/twitter_analyzer.py --username [handle] --tweets 100
The system fetches:
- User profile (followers, bio, verification status)
- Recent tweets (up to 100)
- Engagement metrics (likes, RTs, replies, quotes, views)
Calculates:
- Engagement rate (weighted by follower count)
- Content patterns (hashtag usage, thread frequency, tweet types)
- Posting schedule optimization
- Viral content identification (outliers >2σ above mean)

Key Metrics:

Engagement Rate: (likes + RTs + replies) / followers × 100
Like/RT Ratio: Indicates passive vs. active engagement
Thread Performance: Threads vs. standalone tweet comparison
Viral Multiplier: How many times above average a tweet performed

Output Structure:

TWITTER ANALYSIS: @username
├── Profile metrics (followers, tweets, verification)
├── Engagement metrics (rates, averages, ratios)
├── Viral content (top 5 tweets with multiplier)
├── Thread analysis (performance comparison)
├── Hashtag performance (which hashtags drive engagement)
├── Posting schedule (best times based on data)
└── Recommendations (7 actionable insights)

2. Niche Detection Phase

Objective: Identify a creator's content niche and posting style.

Process:

Run python scripts/profile_analyzer.py --profile @username
Analyzes last 30 tweets for:
- Keyword frequency across 14 predefined niches
- Content themes (most common topics)
- Tone analysis (professional, casual, educational, entertaining)
- Posting cadence and consistency

Niche Categories:

Tech, AI/ML, Crypto/Web3, Business, Marketing
Gaming, Fitness, Beauty, Food, Travel
Comedy, Education, Music, Art

Scoring Method:

niche_score = Σ(keyword_matches) for niche in all_niches
primary_niche = max(niche_scores)
secondary_niches = scores > (primary_score × 0.5)

3. Trend Discovery Phase

Objective: Find viral content and trending topics in a specific niche.

Process:

Run python scripts/trend_aggregator.py --niche "[topic]" --viral-examples --limit 10
Search for tweets matching: "{niche}" min_faves:1000 -is:retweet
Rank by total engagement: likes + (retweets × 2) + (replies × 1.5)
Analyze viral factors:
- Hashtag usage patterns
- Tweet length optimization
- Thread vs. single tweet
- Question-based engagement
- Quote tweet ratio (conversation starter indicator)

Viral Factor Detection:

if len(hashtags) > 0: "used {n} hashtags"
if '?' in text: "engaged audience with question"
if len(text) > 200: "detailed/thorough content"
elif len(text) < 100: "concise and punchy"
if quotes > retweets/2: "sparked conversation"

4. Competitive Intelligence Phase

Objective: Identify top performers and rising accounts in a niche.

Process:

Run python scripts/trend_aggregator.py --niche "[topic]" --find-accounts --limit 10
Aggregate top 50 viral tweets in niche
Group by author and calculate:
- Total engagement across all tweets
- Average engagement per tweet
- Follower count
Sort by engagement/follower ratio (efficiency metric)

Account Scoring:

account_score = (total_engagement / follower_count) × tweet_frequency
# Identifies accounts that punch above their weight

5. Thread Intelligence Phase NEW

Objective: Identify high-performing threads and track engagement from influential accounts.

Process:

Run python scripts/thread_intelligence.py --username [handle] --tweets 50 --threshold 10000
Fetches user's timeline and identifies multi-tweet threads
For each thread:
- Gets full thread context
- Fetches all replies
- Identifies high-value repliers (accounts with >10K followers by default)
- Tracks engagement patterns
Ranks threads by number of high-value replies

Influence Threshold:

high_value_account = follower_count >= threshold  # Default: 10,000
# Configurable via --threshold parameter

Output Structure:

THREAD INTELLIGENCE: @username
├── Thread Statistics (total, high-value reply count, engagement rate)
├── Top Threads (ranked by high-value replies)
│   ├── Thread text preview
│   ├── Tweet count in thread
│   ├── Total replies vs high-value replies
│   └── Reply engagement score
├── Top Thread Details (deep-dive on #1 thread)
│   ├── Full text preview
│   ├── High-value repliers list
│   └── Follower counts
└── Most Engaged High-Value Accounts (across all threads)
    ├── Reply count per account
    └── Number of threads engaged with

Comparison Mode:

python scripts/thread_intelligence.py --username [handle] --compare --tweets 50

Compares thread performance vs standalone tweets to determine optimal content format.

6. Follower Intelligence Phase NEW

Objective: Discover VIP followers using combined influence scoring and engagement tracking.

Process:

Run python scripts/follower_intelligence.py --username [handle] --tweets 20 --max-followers 500
Fetches user's followers (newest first, up to 500)
Tracks engagement across recent tweets:
- Who retweeted (via get_tweet_retweeters endpoint)
- Who replied (via get_tweet_replies endpoint)

Calculates influence score for each follower:

influence_score = (followers × 0.7) + (engagement_count × 1000 × 0.3)

Identifies special segments:
- VIP Followers: Top 50 by influence score
- Hidden Gems: <5K followers but ≥2 interactions
- Top Engagers: Most interactions regardless of follower count

Influence Score Formula:

# Balanced scoring: audience size (70%) + actual engagement (30%)
influence = (follower_count × 0.7) + (total_interactions × 1000 × 0.3)

# Example:
# Account A: 100K followers, 0 interactions = 70,000 influence
# Account B: 10K followers, 5 interactions = 8,500 influence
# Account C: 2K followers, 10 interactions = 4,400 influence (hidden gem!)

Output Structure:

VIP FOLLOWERS: @username
├── Engagement Statistics
│   ├── Total followers analyzed
│   ├── Engaged followers (who interacted)
│   └── Engagement rate %
├── Top VIP Followers (by influence score)
│   ├── Username, follower count, verified status
│   ├── Engagement breakdown (RTs, replies)
│   └── Influence score
├── Hidden Gems (high engagement, low followers)
│   └── Rising creators to nurture
└── Top Engagers (most interactions)
    └── Your biggest supporters

Growth Analysis Mode:

python scripts/follower_intelligence.py --username [handle] --growth --max-followers 200

Analyzes follower quality distribution (micro, small, medium, large, mega).

7. AI Content Generation Phase NEW

Objective: Use AI to analyze viral patterns, draft tweets, and optimize content using Claude 3.5 Sonnet.

Three AI Actions:

A. Viral Pattern Analysis

python scripts/content_generator.py --action analyze --username [top_creator] --tweets 50 --min-engagement 100

Process:

Fetches high-engagement tweets (>100 engagement by default)
Filters for viral content
Sends top 5 tweets to AI with prompt:
- "Analyze content themes that perform best"
- "Identify tweet structure patterns"
- "Determine optimal posting times"
- "Evaluate hashtag strategy"
- "Understand engagement patterns"

Output: AI-generated multi-section analysis with actionable insights.

B. Tweet Drafting

python scripts/content_generator.py --action draft --topic "Your topic here" --username [style_reference] --variations 5

Process:

Optionally analyzes reference account's style (if --username provided)
Sends topic + style context to AI
AI generates 3-5 variations with:
- Different angles/hooks per variation
- Character count (ensures ≤280)
- Strategy explanation
- Predicted engagement level

Output: JSON array of tweet variations with metadata.

C. Tweet Optimization

python scripts/content_generator.py --action optimize --text "Your tweet draft" --goal engagement

Goals: engagement, reach, replies, clarity

Process:

Sends original tweet + optimization goal to AI
AI provides:
- Optimized version
- 2-3 alternative approaches
- Explanation of improvements
- Posting strategy tips

Output: Enhanced tweet with detailed optimization rationale.

8. Enhanced Viral Analysis IMPROVED

The viral factor detection has been significantly enhanced with multi-dimensional pattern analysis:

Previous (Simple):

if len(hashtags) > 0: "used hashtags"
if '?' in text: "question"

New (Sophisticated):

# 1. FORMAT DETECTION
- Thread detection (🧵, "thread", "1/")
- Question count (single vs multiple)
- List/numbered format (1. 2. 3.)
- Emotional hooks (amazing, shocking, breaking)
- Call-to-action (let me know, check out, reply with)

# 2. MEDIA DETECTION
- Visual content presence (images/videos)

# 3. LENGTH OPTIMIZATION
- Comprehensive (>240 chars)
- Concise (<80 chars)
- Optimal range (120-180 chars)

# 4. HASHTAG STRATEGY
- Strategic use (3+ hashtags)
- Focused single hashtag

# 5. ENGAGEMENT PATTERN ANALYSIS
- High reply ratio (>25% = discussion starter)
- High retweet ratio (>20% = shareable)
- Viral coefficient (quotes+RTs >30%)

# 6. TEMPORAL ANALYSIS
- Peak posting window (9-11 AM, 1-3 PM)
- Low-competition hours (9 PM - 6 AM)
- Weekend timing advantage

# 7. ADVANCED PATTERNS
- Data-driven credibility (study, research, analysis)
- Storytelling hooks (story, remember when)
- Controversy/debate (unpopular opinion, hot take)

Example Enhanced Output:

🔥 Why viral: Question encouraging replies; emotional hook driving curiosity;
comprehensive detail (long-form); highly shareable content

Returns top 4 most relevant factors for each viral tweet.

Engagement Scoring Framework

Following head-of-content methodology, we use weighted engagement metrics:

WEIGHTS = {
    'bookmarks': 4.0,  # Strongest intent signal
    'replies': 2.0,    # Direct conversation
    'retweets': 1.5,   # Amplification
    'quotes': 2.5,     # Conversation + amplification
    'likes': 1.0,      # Baseline engagement
    'views': 0.01      # Reach indicator
}

engagement_score = Σ(metric × weight)

Outlier Detection: Content scoring above mean + (2.0 × standard_deviation) is flagged as viral.

Output Formats

Text Output (default)

Human-readable reports with:

Section headers and dividers
Bullet points for key insights
Numerical rankings
Actionable recommendations

JSON Output

Machine-readable data for:

Integration with other tools
Historical tracking
Custom dashboard creation
Multi-account comparison

Example:

python scripts/twitter_analyzer.py --username handle --output json > analysis.json

Configuration

Config File (config.yaml - optional):

# AI content generation settings
openrouter:
  default_model: "anthropic/claude-3.5-sonnet"
  temperature: 0.7
  max_tokens: 2000

# Influence scoring for follower/thread intelligence
influence:
  follower_weight: 0.7           # 70% weight on follower count
  engagement_weight: 0.3         # 30% weight on engagement
  high_value_threshold: 10000    # 10K+ followers = high-value
  hidden_gem_threshold: 5000     # <5K followers = potential gem
  min_engagement_interactions: 2 # Minimum interactions to count

# Viral analysis thresholds
viral:
  min_engagement: 100            # Minimum total engagement
  min_likes: 500                 # For trending searches
  high_reply_ratio: 0.25         # >25% replies = discussion
  high_retweet_ratio: 0.20       # >20% RTs = shareable
  viral_coefficient: 0.30        # >30% quotes+RTs = viral

# Tweet generation defaults
generation:
  num_variations: 5              # Default tweet variations
  max_length: 280                # Twitter character limit
  style_sample_size: 10          # Tweets to analyze for style

settings:
  rate_limit: 100                # Requests per minute
  default_timeframe: "30d"       # Analytics window
  cache_duration: 15             # Minutes to cache trends

Error Handling

Rate Limiting:

Automatic backoff when hitting API limits
60-second cooldown before retry
Progress maintained across retries

Authentication Failures:

If authentication errors occur, check platform configuration

Network Timeouts:

10-second timeout per request
Automatic retry with exponential backoff
Graceful degradation (returns partial results)

Invalid Usernames:

Could not fetch info for @username

→ Verify account exists and is not suspended

Advanced Usage

Batch Analysis

Analyze multiple accounts:

for account in account1 account2 account3; do
    python scripts/twitter_analyzer.py --username $account --output json > ${account}_analysis.json
done

Automated Monitoring

Daily trend tracking:

# Add to crontab
0 9 * * * cd /path/to/skill && python scripts/trend_aggregator.py --niche "AI" --viral-examples --limit 10 >> daily_trends.log

Comparative Analysis

Compare two accounts:

python scripts/twitter_analyzer.py --username account1 --output json > a1.json
python scripts/twitter_analyzer.py --username account2 --output json > a2.json
# Then compare engagement_rate, viral_multiplier, etc.

Related Scripts

Core Analytics:

scripts/twitter_analyzer.py - Comprehensive account analysis
scripts/profile_analyzer.py - Niche detection and content classification
scripts/trend_aggregator.py - Viral content and account discovery (enhanced)
scripts/analytics_calculator.py - Historical performance metrics

Advanced Intelligence (NEW):

scripts/thread_intelligence.py - Thread analysis and high-value engagement tracking
scripts/follower_intelligence.py - VIP follower discovery with influence scoring
scripts/content_generator.py - AI-powered viral analysis and tweet generation

Infrastructure:

scripts/api_client.py - Core Twitter API wrapper (enhanced with 10+ endpoints)
scripts/ascii_formatter.py - Beautiful terminal dashboard formatting
scripts/test_new_features.py - Test suite for validation
scripts/setup_config.py - Interactive configuration wizard

Integration with Other Skills

This skill complements:

Content planning skills: Use viral patterns to inform content strategy
Copywriting skills: Analyze successful tweet structures
Marketing skills: Understand audience engagement patterns

Metrics Glossary

Engagement Rate: % of followers who interact with content
Viral Multiplier: How many standard deviations above average
Like/RT Ratio: Passive (likes) vs. active (RTs) engagement
Thread Performance: Avg engagement on threaded vs. single tweets
Consistency Score: Regularity of posting (0-10 scale)
Quote Rate: Replies with quotes (conversation quality indicator)

Best Practices

Run weekly analysis on your account to track trends
Compare to competitors in your niche for benchmarking
Act on viral patterns - replicate what works
Monitor recommended posting times based on your data
Track hashtag performance and iterate
Experiment with threads if data shows they outperform
Focus on engagement rate over vanity metrics

Troubleshooting

"Rate limit exceeded" → Wait 60 seconds and retry

"Request timed out" → Reduce --tweets parameter or try again (network issue)

Empty results → Try broader niche keywords or lower min_faves threshold

Proxy connection issues → Check that sc-proxy is running and configured correctly in Star Child

What's New in v2.0:

Thread Intelligence: Identify high-value engagement in threads (10K+ followers)
Follower Intelligence: VIP follower discovery with influence score algorithm
AI Content Generation: OpenRouter integration for viral analysis & tweet drafting
Enhanced Viral Analysis: 7-category sophisticated pattern detection
API Expansion: 10+ new TwitterAPI.io endpoints (followers, retweeters, replies, threads)
ASCII Dashboards: Beautiful terminal visualizations with progress bars
Comprehensive Config: Documented settings for all thresholds and parameters

Module Summary:

twitter_analyzer.py - Account analytics (v1.0 feature)
profile_analyzer.py - Niche detection (v1.0 feature)
trend_aggregator.py - Viral discovery (enhanced in v2.0)
thread_intelligence.py - Thread analysis (NEW in v2.0)
follower_intelligence.py - VIP follower tracking (NEW in v2.0)
content_generator.py - AI-powered content (NEW in v2.0)

creator-insights

Twitter Creator Insights

When to Use This Skill

Core Workflow

1. Account Analysis Phase

2. Niche Detection Phase

3. Trend Discovery Phase

4. Competitive Intelligence Phase

5. Thread Intelligence Phase NEW

6. Follower Intelligence Phase NEW

7. AI Content Generation Phase NEW

A. Viral Pattern Analysis

B. Tweet Drafting

C. Tweet Optimization

8. Enhanced Viral Analysis IMPROVED

Engagement Scoring Framework

Output Formats

Text Output (default)

JSON Output

Configuration

Error Handling

Advanced Usage

Batch Analysis

Automated Monitoring

Comparative Analysis

Related Scripts

Integration with Other Skills

Metrics Glossary

Best Practices

Troubleshooting

What's New in v2.0:

Module Summary: