Twitter Algorithm Skill

Optimize tweets for organic reach using insights from Twitter's open-source algorithm.

Overview

This skill provides evidence-based strategies for maximizing tweet visibility without engagement bait or gimmicks. Based on analysis of twitter/the-algorithm source code.

Quick Reference

The Golden Rules

1. 8-hour half-life — Early engagement compounds. Post when you can engage.
2. Replies > Quotes > RTs > Likes — Prioritize high-signal engagement.
3. Native media wins — Upload images/video directly to Twitter.
4. 0-1 hashtags — More triggers spam detection.
5. Ratio matters — High following/low followers = reputation penalty.

Pre-Post Checklist

[ ] Genuine value for my audience?
[ ] First line works as standalone hook?
[ ] Native media (not external links)?
[ ] 0-1 hashtags maximum?
[ ] Available to engage for next 1-2 hours?
[ ] Specific topic (not generic)?

How It Works

SimClusters (Virality Engine)

Twitter groups users into 145K interest communities. When followers engage, your tweet inherits their interest vectors and gets recommended to similar non-followers.

Implication: Specific topics spread better. "AI agents on Base" > "technology is cool"

TweepCred (Reputation Score)

PageRank-based reputation. Quality of followers matters more than quantity.

The ratio penalty:

following=5000, followers=100 → reputation ÷ 50x
following=200, followers=2000 → strong reputation signal

Engagement Decay

Half-life: 8 hours
Hour 1: 100% weight
Hour 8: 50% weight  
Hour 16: 25% weight

Early engagement compounds. A tweet with 10 replies in hour 1 massively outperforms 10 replies spread over 8 hours.

Content Guidelines

What Gets Boosted

• HAS_NATIVE_IMAGE / HAS_NATIVE_VIDEO (explicit signals in code)
• High engagement velocity
• Engagement from high-reputation accounts
• Content matching follower interest clusters
• Replies and conversations

What Gets Killed

Signal	Impact
2+ hashtags	Spam flag
High reply:like ratio	"Ratio'd" = suspicious
"See fewer" feedback	0.2x for 140 days
External links	Neutral to negative
ALL CAPS	Quality penalty
New account	"NotGraduated" demotion

Timing Strategy

Best Windows (US Tech Audience)

• Morning: 8-10am PT
• Lunch: 12-2pm PT
• Evening: 6-8pm PT

The 2-Hour Rule

First 2 hours determine a tweet's trajectory. Stay present to:

• Reply to early commenters (boosts their engagement + yours)
• Answer questions (drives more replies)
• Thank people thoughtfully (encourages more interaction)

Scripts

Tweet Scorer

Score a draft tweet against algorithm signals:

./scripts/score-tweet.sh "Your tweet text here"

Output:

Structure Score: 8/10
- Length: ✅ Good (156 chars)
- Hashtags: ✅ None
- Caps: ✅ Normal
- Media: ⚠️ Consider adding image

Timing Score: 7/10
- Current time: 2pm PT ✅ Good window
- Day: Monday ✅ Weekday

Recommendations:
- Add native image for +15-20% reach
- Post now and engage for next 2 hours

Engagement Analyzer

Analyze a posted tweet's performance:

./scripts/analyze-tweet.sh <tweet_id>

Optimal Time Calculator

Find best posting time for your audience:

./scripts/best-time.sh

Integration

With Cron Jobs

Add to your twitter posting cron:

Read ~/path/to/skills/twitter-algorithm/SKILL.md before composing tweets.
Run score-tweet.sh on drafts before posting.

Pre-Post Validation

import { scoreTweet } from './scripts/score-tweet.mjs';

const score = scoreTweet(draft);
if (score.total < 6) {
  console.log('Revise:', score.recommendations);
}

Anti-Patterns

Never do these:

• "Like if you agree" (engagement bait, algorithm tracks this)
• Multiple hashtags (spam signal)
• Follow/unfollow games (kills reputation)
• Posting and disappearing (wastes the 8-hour window)
• ALL CAPS (quality penalty)
• Repetitive content (spam flag)

References

• references/ranking-signals.md — Full engagement weight analysis
• references/virality-mechanics.md — SimClusters and For You algorithm
• references/full-playbook.md — Complete strategic playbook

Source

Based on analysis of:

• twitter/the-algorithm (open source)
• src/scala/com/twitter/home_mixer/ (home timeline ranking)
• src/scala/com/twitter/cr_mixer/ (content recommendations)
• src/scala/com/twitter/simclusters_v2/ (interest clustering)

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No gimmicks. The algorithm rewards quality because quality drives engagement.