Agent skill · marketing growth · composiohq
twitter-algorithm-optimizer
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.
Why this skill is useful
Provides specific strategies and insights based on Twitter's ranking algorithms that the AI wouldn't reliably generate on its own.
What it needs
Requires twitter account access. About 7k tokens when loaded. Last updated 2026-07-24. 71,973 stars on the source repository.
What this skill does
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 specific communities Key strategy: Make content that appeals to tight communities who will engage TwHIN: Knowledge graph embeddings for users and posts Maps relationships between users and content topics Helps Twitter understand if your tweet fits your follower interests Key strategy: Stay in your niche or clearly signal topic shifts Tweepcred: User reputation/authority scoring Higher-credibility users get more distribution Your past engagement history affects current tweet reach Key strategy: Build reputation through consistent engagement Engagement Signals Tracked Twitter's Unified User Actions …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills composiohq/twitter-algorithm-optimizer