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Optimize tweets for organic reach using insights from Twitter's open-source algorithm. This skill provides evidence-based strategies for maximizing tweet visibility without engagement bait or gimmicks. Based on analysis of the twitter/the-algorithm source code
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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| name | twitter-algorithm |
| description | Optimize tweets for organic reach using insights from Twitter's open-source algorithm. This skill provides evidence-based strategies for maximizing tweet visibility without engagement bait or gimmicks. Based on analysis of the twitter/the-algorithm source code |
This skill provides evidence-based strategies for maximizing tweet visibility without engagement bait or gimmicks. Based on analysis of twitter/the-algorithm source code.
[ ] 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)?
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"
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
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.
HAS_NATIVE_IMAGE / HAS_NATIVE_VIDEO (explicit signals in code)| 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 |
First 2 hours determine a tweet's trajectory. Stay present to:
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
Analyze a posted tweet's performance:
./scripts/analyze-tweet.sh <tweet_id>
Find best posting time for your audience:
./scripts/best-time.sh
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.
import { scoreTweet } from './scripts/score-tweet.mjs';
const score = scoreTweet(draft);
if (score.total < 6) {
console.log('Revise:', score.recommendations);
}
Never do these:
references/ranking-signals.md — Full engagement weight analysisreferences/virality-mechanics.md — SimClusters and For You algorithmreferences/full-playbook.md — Complete strategic playbookBased 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.