twitter-algorithm-optimizer

Solid

Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.

AI & Automation 2,996 stars 363 forks Updated yesterday MIT

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Skill Content

# Twitter Algorithm Optimizer ## When to Use This Skill Use this skill when you need to: - **Optimize tweet drafts** for maximum reach and engagement - **Understand why** a tweet might not perform well algorithmically - **Rewrite tweets** to align with Twitter's ranking mechanisms - **Improve content strategy** based on the actual ranking algorithms - **Debug underperforming content** and increase visibility - **Maximize engagement signals** that Twitter's algorithms track ## What This Skill Does 1. **Analyzes tweets** against Twitter's core recommendation algorithms 2. **Identifies optimization opportunities** based on engagement signals 3. **Rewrites and edits tweets** to improve algorithmic ranking 4. **Explains the "why"** behind recommendations using algorithm insights 5. **Applies Real-graph, SimClusters, and TwHIN principles** to content strategy 6. **Provides engagement-boosting tactics** grounded in Twitter's actual systems ## How It Works: Twitter's Algorithm Architecture Twitter's recommendation system uses multiple interconnected models: ### Core Ranking Models **Real-graph**: Predicts interaction likelihood between users - Determines if your followers will engage with your content - Affects how widely Twitter shows your tweet to others - Key signal: Will followers like, reply, or retweet this? **SimClusters**: Community detection with sparse embeddings - Identifies communities of users with similar interests - Determines if your tweet resonates within sp...

Details

Author
davepoon
Repository
davepoon/buildwithclaude
Created
10 months ago
Last Updated
yesterday
Language
Python
License
MIT

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