X Impact Checker

Workspace Context

Read bootstrap context before asking questions: strategy/brand.md for brand, audience, offer, channels, tools, constraints, and metrics; about/me.md for personal voice; content/ideas.md and content/calendar.md for content planning. Use legacy product-marketing context files only as fallback. Save generated drafts to content/<platform>/drafts/YYYY-MM-DD_short-topic-slug.md, and route durable learnings back to strategy/brand.md, about/me.md, or content/ideas.md.

Operating Contract

This skill is self-contained for its frontmatter scope: use its local instructions, references, scripts, and assets as the playbook; ask only for missing task-specific inputs; hand off to adjacent skills instead of expanding scope; and return an actionable artifact, decision, plan, draft, or diagnostic.

Analyze X posts for viral potential using a 19-element scoring system and heuristics inspired by X's published recommendation architecture.

When to Use

• Score a tweet draft before publishing
• Understand why a tweet underperformed
• Rewrite tweets to align with Twitter's ranking mechanisms
• Build topical authority in a specific niche
• Debug inconsistent engagement rates

Scoring System (100 points)

Tier 1: Core Engagement (60 points)

Tier 2: Extended Engagement (25 points)

Tier 3: Relationship Building (15 points)

Penalties (subtract from total)

Grades

Output Format

Progress Tracking

Show analysis progress when the host environment supports task tracking:

1. Analyzing post content (in_progress → completed)
2. Calculating scores across all elements (in_progress → completed)
3. Generating top 5 priority improvements (in_progress → completed)
4. Creating optimized version (in_progress → completed)

Report Structure

1. Score: 🎯 XX/100 (Grade: X)

2. Breakdown Table:

| Category | Factor | Score | Max | Assessment |
|----------|--------|-------|-----|------------|
| **💬 Core Engagement** | | | 60 | |
| | 💭 Reply Potential | X/22 | 22 | [reason] |
| | 🔄 Retweet Potential | X/16 | 16 | [reason] |
| | ❤️ Favorite Potential | X/12 | 12 | [reason] |
| | 💬 Quote Potential | X/10 | 10 | [reason] |
| **⏱️ Extended Engagement** | | | 25 | |
| | 👀 Dwell Time | X/6 | 6 | [reason] |
| | ⏳ Continuous Dwell Time | X/4 | 4 | [reason] |
| | 🔗 Click Potential | X/5 | 5 | [reason] |
| | 🖼️ Photo Expand | X/4 | 4 | [reason] |
| | 🎥 Video View | X/3 | 3 | [reason] |
| | 🔍 Quoted Click | X/3 | 3 | [reason] |
| **🤝 Relationship Building** | | | 15 | |
| | 👤 Profile Click | X/5 | 5 | [reason] |
| | ➕ Follow Potential | X/4 | 4 | [reason] |
| | 📤 Share Potential | X/2 | 2 | [reason] |
| | 💌 Share via DM | X/2 | 2 | [reason] |
| | 📋 Share via Link | X/2 | 2 | [reason] |
| **⚠️ Negative Signals** | | | | |
| | 😐 Not Interested Risk | -X | 0 to -15 | [reason] |
| | 🔇 Mute Risk | -X | 0 to -15 | [reason] |
| | 🚫 Block Risk | -X | 0 to -25 | [reason] |
| | 🚨 Report Risk | -X | 0 to -30 | [reason] |
| **🏆 TOTAL** | | **XX/100** | | **Grade: X** |

3. 📈 Top 5 Priority Improvements: Specific, actionable suggestions across different categories

4. ✨ Optimized Version: Rewritten post with improvements applied (in original language)

Algorithm Architecture

Understanding the underlying models helps explain why the scoring works.

Core Ranking Models

Real-graph — Predicts interaction likelihood between users

• Determines if your followers will engage with your content
• Strategy: Make content your specific follower segment will engage with

SimClusters — Community detection with sparse embeddings

• Identifies communities with similar interests; your tweet resonates within these clusters
• Strategy: Pick ONE clear topic and serve tight communities deeply

TwHIN — Knowledge graph embeddings mapping users and content topics

• Helps Twitter understand if your tweet fits your established identity
• Strategy: Stay in your niche or clearly signal topic shifts

Tweepcred — User reputation/authority scoring

• Your past engagement history affects current tweet reach
• Strategy: Build through consistent quality, not engagement bait

Engagement Signals

Explicit (high weight): Likes, replies, retweets, quote tweets

Implicit (also weighted): Profile visits, link clicks, dwell time, saves/bookmarks

Negative: Block/report (heavily penalized), mute/unfollow, quick scroll-past

Optimization by Algorithm Layer

Detailed Scoring Criteria

Reply Potential (22 pts)

• Direct questions, debatable claims, opinion invitations
• ❌ "Just shipped a new feature." → ✅ "Should features ship fast but buggy, or slow but stable? We chose speed—was it the right call?"

Retweet Potential (16 pts)

• Actionable insights, surprising facts, numbered lists, data-driven content
• ❌ "I learned something today." → ✅ "🧵 3 React patterns that cut my bundle size by 30%: 1. Lazy loading hooks 2. Code splitting by route 3. Tree-shaking unused exports"

Favorite Potential (12 pts)

• Emotional resonance, personal stories, relatable moments, vulnerability
• ❌ "Debugging is hard." → ✅ "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 pts)

• Strong opinions, challenges conventional wisdom, clear stances
• ❌ "TypeScript is useful." → ✅ "TypeScript's biggest value isn't catching bugs—it's documentation. The type errors are just a bonus. Fight me."

Dwell Time (6 pts)

• Long-form content requiring reading time; detailed explanations; technical depth

Continuous Dwell Time (4 pts)

• Thread indicators (🧵, "1/"), narrative structure, complexity requiring re-reading
• ❌ "Here's how I built X." → ✅ "🧵 How I went from idea to $10k MRR in 30 days (1/8)\n\nDay 1-7: Validation..."

Profile Click (5 pts)

• Creates author curiosity; demonstrates expertise; credibility signals
• ❌ "I think React is good." → ✅ "After architecting React apps for Airbnb, Netflix, and 50+ startups, here's what I wish I knew on day one:"

Follow Potential (4 pts)

• Demonstrates ongoing value; establishes content cadence
• ❌ "Here's a React tip." → ✅ "React tip #47: [insight]\n\nI break down advanced React patterns every Monday."

Score Normalization

Final Score = Base Score (0-100) + Penalties (-75 to 0)
Normalized Score = max(0, min(100, Final Score))

Penalty capping: total penalties > -20 causes gradual dampening; hard cap at -75.

Text Analysis Limitations

This skill performs heuristic text-based analysis, not ML prediction. It cannot detect actual media presence, real engagement metrics, author follower count, or network graph relationships. Best used for pre-publishing optimization, not post-hoc analytics.

Language Handling

Detect input language. Respond in same language. Keep optimized version in original language.

When input is in Japanese, display Category and Factor names as: 日本語訳（English Original）

Japanese translations:

• 💬 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).