Twitter Algorithm Optimizer
This analyzes your tweet drafts against Twitter's actual open-source recommendation algorithms (Real-graph, SimClusters, TwHIN) and rewrites them to maximize reach. You get specific optimizations based on how the algorithm weighs engagement signals like replies versus retweets, plus explanations grounded in Twitter's ranking mechanics rather than generic engagement advice. Use it when you need a tweet to perform well and want to understand why certain phrasings work better algorithmically. The approach is data-driven but still requires you to know your audience, since the algorithm fundamentally optimizes for what your specific followers will engage with. Worth trying if you're tired of guessing what makes tweets spread.
- skills.sh installs
- 5.1K
- GitHub stars
- 79.2K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add composiohq/awesome-claude-skills --skill twitter-algorithm-optimizer --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
- Analyzes tweets against Twitter's core recommendation algorithms
- Identifies optimization opportunities based on engagement signals
- Rewrites and edits tweets to improve algorithmic ranking
- Explains the "why" behind recommendations using algorithm insights
- Applies Real-graph, SimClusters, and TwHIN principles to content strategy
- 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
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