Does it support Twitter threads or just single tweets?

It supports both, offering specific strategies for maintaining high engagement levels across multiple posts to ensure the entire thread is surfaced by the algorithm.

Can this skill help me avoid being shadow-banned?

Yes, it identifies negative signals such as engagement bait, low-reputation patterns, and inflammatory content that typically trigger algorithmic penalties or reduced visibility.

Is this based on the latest Twitter algorithm updates?

The skill is grounded in the architectural principles revealed in Twitter's open-source release, including how the system weights likes, replies, and retweets.

How does this skill use Twitter's algorithm insights?

The skill applies logic derived from Twitter's open-source recommendation code, focusing on how models like SimClusters and Real-graph categorize and rank content based on user relationships and interests.

Do I need to be a developer to use this skill?

No, while it uses technical insights, it provides clear, actionable feedback and tweet rewrites that anyone can use to improve their social media performance.

Twitter Algorithm Optimizer

Name: Twitter Algorithm Optimizer
Author: ComposioHQ

byComposioHQ

•

25,354

Gestión de Redes Sociales

Optimizes tweet drafts and content strategies using insights from Twitter's open-source recommendation engine to maximize reach and engagement.

Acerca de

The Twitter Algorithm Optimizer is a specialized skill that applies technical principles from Twitter's open-source recommendation system—such as Real-graph, SimClusters, and TwHIN—to improve tweet performance. It analyzes your drafts against known ranking models to predict engagement likelihood, identifies community-specific resonance opportunities, and provides actionable rewrites. Whether you are launching a product or building a personal brand, this skill helps you navigate engagement signals and avoid negative ranking factors to ensure your content reaches the right audience effectively.

Características Principales

Tweepcred reputation assessment and improvement strategies
Niche-specific content tailoring for targeted community resonance
Negative signal detection to prevent algorithmic de-prioritization
25,354 GitHub stars
Algorithmic analysis based on Real-graph, SimClusters, and TwHIN models
Engagement signal optimization for likes, replies, and retweets

Casos de Uso

Drafting high-engagement threads that maintain visibility through the ranking pipeline
Debugging underperforming content strategies by identifying algorithmic bottlenecks
Optimizing technical product launches to reach specific developer communities

Acerca de

Características Principales

Tweepcred reputation assessment and improvement strategies
Niche-specific content tailoring for targeted community resonance
Negative signal detection to prevent algorithmic de-prioritization
25,354 GitHub stars
Algorithmic analysis based on Real-graph, SimClusters, and TwHIN models
Engagement signal optimization for likes, replies, and retweets

Casos de Uso

Drafting high-engagement threads that maintain visibility through the ranking pipeline
Debugging underperforming content strategies by identifying algorithmic bottlenecks
Optimizing technical product launches to reach specific developer communities