⚠️ Better: "I learned React hooks can reduce bundle size by 30%."
✅ Best: "🧵 3 React patterns that cut my bundle size by 30%:\n\n1. Lazy loading hooks\n2. Code splitting by route\n3. Tree-shaking unused exports"
Favorite Potential (12 points)
Evaluation Criteria:
Emotional resonance: joy, frustration, triumph
Personal stories: "When I was..."
Relatable moments: "We've all been there..."
Inspirational content
Vulnerability and authenticity
Useful references worth saving
Improvement Strategies:
❌ Bad: "Debugging is hard."
⚠️ Better: "Spent 3 hours debugging a typo."
✅ Best: "Spent 3 hours debugging a production issue. The fix? A missing semicolon I added during 'quick cleanup' at 2am. Never touching working code past midnight again 😅"
Quote Potential (10 points)
Evaluation Criteria:
Strong opinions: "X is dead", "Y is overrated"
Challenges conventional wisdom
Invites commentary and counter-arguments
Takes clear stance on controversial topics
Thought-provoking perspectives
Improvement Strategies:
❌ Bad: "TypeScript is useful."
⚠️ Better: "TypeScript prevents bugs."
✅ Best: "TypeScript's biggest value isn't catching bugs—it's documentation. The type errors are just a bonus. Fight me."
Tier 2: Extended Engagement
Dwell Time (6 points)
Evaluation Criteria:
Long-form content requiring reading time
Detailed explanations with examples
Technical depth
Multi-paragraph structure
Educational content
Improvement Strategies:
Add concrete examples: "For instance, when building X..."
Include numbers and data: "This reduced latency from 200ms to 50ms"
Structure with clear sections
Continuous Dwell Time (4 points)
Evaluation Criteria:
Thread indicators: "🧵", "Thread:", "1/", numbered series
✅ Best: "🧵 How I went from idea to $10k MRR in 30 days (1/8)\n\nDay 1-7: Validation\nDays 8-14: MVP\nDays 15-30: Launch\n\nHere's what nobody tells you..."
Visual language: "see", "look", "view", "check this out"
Emojis suggesting visuals: 📸, 🎨, 👀, 📷, 🖼️
Before/after comparisons
Multiple image storytelling: "Swipe through..."
Visual evidence: "Here's proof 👇"
Improvement Strategies:
❌ Bad: "My dashboard looks great now."
⚠️ Better: "Check out my new dashboard design."
✅ Best: "Before/after of my analytics dashboard redesign 👇\n\nWent from cluttered mess to clean insights in 2 days.\n\n[visual indicators suggest images present]"
Video View Potential (3 points)
Evaluation Criteria:
Video markers: [video], "▶️", "watch", "tutorial", "demo"
Professional sharing context (Slack, email, bookmarks)
"Save this" or "Bookmark" language
Educational/tutorial content
Resource library worthy
Improvement Strategies:
❌ Bad: "Here are some Git commands I use."
⚠️ Better: "Useful Git commands for daily work."
✅ Best: "📌 Bookmark this: 15 Git commands that saved me 100+ hours this year\n\n[Well-structured list with examples]\n\nPrint this and keep it next to your monitor."
Score Normalization
The algorithm applies normalization to balance positive and negative signals:
Final Score = Base Score (0-100) + Penalties (-75 to 0)
Normalized Score = max(0, min(100, Final Score))
Penalty Capping:
Total penalties ≤ -20: Applied at full weight
Total penalties > -20: Gradual dampening begins
Total penalties > -75: Hard cap at -75 to prevent over-penalization
This prevents a single negative signal from completely dominating the score while maintaining their importance in the algorithm.
Text Analysis Limitations
This skill performs heuristic text-based analysis, not ML prediction.
What This Skill Cannot Detect
Missing Metadata:
Actual media presence (photos, videos)
Real video duration or quality
Actual click-through rates
True engagement metrics
Author reputation/follower count
Tweet timestamps or virality history
Cannot Access:
Phoenix ML model predictions
User interaction history
Network graph relationships
Real-time engagement signals
What This Skill Infers From
Text-Based Heuristics:
Language patterns and structure
Content formatting (threads, lists, etc.)
Emotional tone and style
Visual indicators (emojis, markdown)
Call-to-action strength
Question vs. statement structure
Scoring Approach:
Conservative
Unknown elements get baseline scores
Pattern-Based
Detects language cues (e.g., 📸 for photos, 🧵 for threads)
Optimization-Focused
Best used for pre-publishing content improvement
Best Use Case
Pre-publishing optimization to maximize engagement potential, not post-hoc analytics or prediction of actual engagement numbers.
Language Handling
Detect input language. Respond in same language. Keep optimized version in original language.
Bilingual Display for Category and Factor Names
When input is in Japanese:
Display Category and Factor names as:
日本語訳(English Original)
Examples:
Category:
コアエンゲージメント(Core Engagement)
Factor:
返信潜在力(Reply Potential)
Factor:
リツイート潜在力(Retweet Potential)
When input is in English:
Display Category and Factor names in English only
Examples:
Category:
Core Engagement
Factor:
Reply Potential
Japanese translations with emojis for reference:
💬 Core Engagement → コアエンゲージメント
⏱️ Extended Engagement → 拡張エンゲージメント
🤝 Relationship Building → 関係構築
⚠️ Negative Signals → ネガティブシグナル
💭 Reply Potential → 返信潜在力
🔄 Retweet Potential → リツイート潜在力
❤️ Favorite Potential → いいね潜在力
💬 Quote Potential → 引用潜在力
👀 Dwell Time → 滞在時間
⏳ Continuous Dwell Time → 継続滞在時間
🔗 Click Potential → クリック潜在力
🖼️ Photo Expand → 写真展開潜在力
🎥 Video View → 動画視聴潜在力
🔍 Quoted Click → 引用クリック潜在力
👤 Profile Click → プロフィールクリック
➕ Follow Potential → フォロー潜在力
📤 Share Potential → 共有潜在力
💌 Share via DM → DM経由共有
📋 Share via Link → リンクコピー共有
😐 Not Interested Risk → 興味なしリスク
🔇 Mute Risk → ミュートリスク
🚫 Block Risk → ブロックリスク
🚨 Report Risk → 報告リスク
Algorithm Reference
See
references/algorithm-weights.md
for complete weight details from X's open-source algorithm (19-element system).