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Tweet Composer

依据 X 公开排序算法的结构规则,对推文草稿按回复潜力、媒体、可分享性、停留时长、格式与负面信号等维度评分(0-100),输出评分卡、预测互动评估、改进建议与优化后的推文文本,并给出主题串和发帖策略要点。完整规则存放于 references 目录的规则文件中。

minilozio
minilozio
热度 68.34
最近更新 2026-05-11 17:27:08
分类:营销依赖:读写文件 / 内部调用大模型应用场景:社交媒体 / 文案创作

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add tweet-composer
Metadata
name
tweet-composer
description
Score and optimize tweets based on X's real open-source ranking algorithm. Analyzes draft tweets against the actual ranking code — not generic tips. Use when: composing tweets, optimizing drafts for reach, planning threads, analyzing why a tweet performed well/poorly, or asking for posting strategy advice.

Tweet Composer

Score and optimize tweets using rules derived from X's open-source ranking algorithm.

How It Works

X's "For You" feed is ranked by a Grok-based transformer (Phoenix) that predicts 19 engagement actions for every candidate tweet. The final score is a weighted sum of these predictions. This skill encodes the structural rules from that pipeline into a scoring system.

For the full algorithm breakdown, read references/algorithm-rules.md.

Scoring a Draft Tweet

When a user asks to score or optimize a tweet draft:

  1. Read references/algorithm-rules.md for the complete rules engine
  2. Analyze the draft against all rules
  3. Output the score card in this format:
🐦 Tweet Composer — Score: XX/100

[Category scores with ✅ ⚠️ ❌ indicators]

📊 Predicted Action Boost:
├─ P(reply): [assessment]
├─ P(favorite): [assessment]  
├─ P(share): [assessment]
├─ P(dwell): [assessment]
└─ P(not_interested): [assessment]

💡 Suggestions:
→ [actionable improvements]

✏️ Optimized version:
"[rewritten tweet]"

Scoring Rubric (Quick Reference)

Score 0-100 based on weighted categories:

Category Weight What to check
Reply potential 25 Questions, opinions, CTAs that drive replies
Media 20 Native image/video attached (not link previews)
Shareability 15 Would someone DM this or copy the link?
Dwell time 15 Length that makes people stop scrolling
Content quality 10 Clear, original, not generic
Format 10 No links in body, no hashtags, good length
Negative signals 5 Risk of not_interested/mute/block

Thread Optimization

When composing threads:

  • First tweet = strongest hook (DedupConversationFilter keeps only the best per conversation)
  • 3-6 tweets max (short threads > mega-threads)
  • Each tweet self-contained (many see only the first)
  • Media on tweet 1 or 2 for photo_expand boost
  • CTA in last tweet

Quick Rules (No Reference File Needed)

  • Links: Always in reply, never in body (learned penalty from lower engagement)
  • Hashtags: Zero. The model learns they reduce engagement
  • Length: 100-200 chars sweet spot for single tweets
  • Media: Native image/video = separate P(photo_expand) and P(video_quality_view) predictions
  • Video: Must exceed minimum duration threshold for VQV weight to apply
  • Timing: Post when your audience is active — engagement velocity in first 30 min is critical
  • Frequency: AuthorDiversityScorer penalizes exponentially: 2nd post ~55% score, 3rd ~33%. Max 3-4 strong tweets/day
  • Quote tweets: P(quote) has dedicated weight — QTs with added value outperform plain retweets
  • Engagement bait: Questions/polls drive P(reply). "What would you add?" > "Like if you agree"