x-boost

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Optimize X/Twitter posts for maximum reach using algorithm insights. Use when writing tweets, improving post engagement, or analyzing why posts underperform.

guzus By guzus schedule Updated 1/20/2026

name: x-boost description: Optimize X/Twitter posts for maximum reach using algorithm insights. Use when writing tweets, improving post engagement, or analyzing why posts underperform.

X Post Optimizer

Helps craft posts optimized for the X recommendation algorithm based on open-source algorithm analysis.

How the Algorithm Scores Posts

The algorithm predicts 19 engagement actions and combines them:

Positive signals (boost reach):

  • Likes, Replies, Retweets, Quotes
  • Dwell time (time spent reading)
  • Profile clicks, Follows from post
  • Shares (DM, copy link)
  • Video quality views, Photo expands

Negative signals (kill reach):

  • "Not interested" clicks
  • Blocks, Mutes, Reports

Instructions

When asked to optimize a post or write for X:

1. Hook First

  • Lead with the most compelling point
  • Stop the scroll in first 5 words
  • Use pattern interrupts

2. Maximize Dwell Time

  • Add depth that rewards reading
  • Use line breaks for scanability
  • Include images/videos that make people pause

3. Encourage Replies

  • End with questions
  • Make takes that invite discussion
  • Leave threads open-ended

4. Avoid Author Penalty

The algorithm applies exponential decay to rapid posts from same author:

score = base_score × decay^(post_count)

Recommendation: Space posts 2-4 hours apart for maximum individual reach.

5. Leverage In-Network Advantage

Posts to followers rank higher than discovery posts. Build genuine following over chasing virality.

Quick Checklist

When reviewing a draft post, check:

  • Hook in first line?
  • Rewards reading (dwell time)?
  • Invites replies?
  • No spam/repetitive content?
  • Authentic voice (not engagement bait)?
  • Appropriate timing from last post?

What Doesn't Work

  • Engagement pods (artificial patterns detected)
  • Keyword stuffing (algorithm learns behavior, not keywords)
  • Rapid-fire posting (author diversity penalty)
  • Controversial content that triggers blocks/mutes

Example Optimization

Before:

Just launched my new product! Check it out at example.com

After:

I spent 6 months building something I wish existed 3 years ago.

The problem: [specific pain point]
The solution: [what you built]

Here's what surprised me most about the process:

[insight that invites discussion]

What's been your experience with [related topic]?

Why it's better:

  • Hook creates curiosity (dwell time)
  • Structure rewards reading
  • Ends with question (replies)
  • Authentic story (avoids mute/block signals)
Install via CLI
npx skills add https://github.com/guzus/go-viral --skill x-boost
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