How does this skill optimize tweets?

It analyzes your content against Twitter's open-source ranking factors, suggesting edits that trigger higher-weight signals like retweets, replies, and bookmarks.

Does this use Twitter's actual algorithm data?

Yes, it applies principles derived from Twitter's released source code, including the Real-graph, SimClusters, and TwHIN recommendation models.

Is this skill meant for professional accounts?

It is designed for any professional, developer, or brand using Claude Code who wants to improve their reach using data-driven, algorithmic insights.

Can it help avoid algorithmic penalties?

Absolutely. The skill identifies 'negative signals' such as inflammatory language or spammy engagement bait that can lead to reduced visibility or shadowbans.

Twitter Algorithm Optimizer

Name: Twitter Algorithm Optimizer
Author: ai-wes

byai-wes

社交媒体管理

Optimizes tweet engagement and reach by applying insights from Twitter's open-source recommendation algorithms.

关于

This skill leverages deep insights into Twitter's open-source recommendation system to help users maximize their content's visibility and engagement. It analyzes tweet drafts against core ranking models like Real-graph, SimClusters, and TwHIN, providing actionable rewrites and strategy advice. Whether you are looking to reach specific niche communities, increase follower interaction, or debug underperforming content, this skill transforms raw thoughts into algorithm-friendly posts that trigger high-weight engagement signals like retweets and replies.

主要功能

Algorithmic analysis of tweet drafts using Real-graph and SimClusters models
0 GitHub stars
Negative signal detection to avoid algorithmic penalties and reach suppression
Community resonance optimization via niche-specific terminology and language
Reputation management through Tweepcred-aligned content strategies
Automated tweet rewriting to maximize engagement signals like likes and replies

使用场景

Debugging underperforming content to identify why the algorithm is suppressing reach
Transforming generic product updates into high-engagement launch announcements
Engineering 'hot takes' that spark healthy community debate and replies

关于

主要功能

Algorithmic analysis of tweet drafts using Real-graph and SimClusters models
0 GitHub stars
Negative signal detection to avoid algorithmic penalties and reach suppression
Community resonance optimization via niche-specific terminology and language
Reputation management through Tweepcred-aligned content strategies
Automated tweet rewriting to maximize engagement signals like likes and replies

使用场景

Debugging underperforming content to identify why the algorithm is suppressing reach
Transforming generic product updates into high-engagement launch announcements
Engineering 'hot takes' that spark healthy community debate and replies