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
- 8-hour half-life — Early engagement compounds. Post when you can engage.
- Replies > Quotes > RTs > Likes — Prioritize high-signal engagement.
- Native media wins — Upload images/video directly to Twitter.
- 0-1 hashtags — More triggers spam detection.
- 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 analysisreferences/virality-mechanics.md— SimClusters and For You algorithmreferences/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)
No gimmicks. The algorithm rewards quality because quality drives engagement.