X Impact Checker

SkillAI & models

Analyze and optimize X (Twitter) posts for viral potential and reach using heuristics inspired by X's published open-source recommendation architecture. Use when user wants to: (1) Check if a post will go viral, (2) Score a tweet for engagement potential, (3) Optimize or rewrite a tweet for algorithmic reach, (4) Understand why a tweet underperformed, (5) Build audience in a specific niche. Triggers: "Check if this will go viral", "Make this post buzz", "Will this tweet perform well?", "Optimize my tweet", "How can I make this viral?", "rewrite this tweet", "improve my tweet engagement", "twitter algorithm", "tweet reach", "debug underperforming tweet", "バズるかチェックして", "Xでバズる投稿にして", "伸びるかチェックして", "この投稿を伸ばして", "投稿を改善して", "ツイートを最適化して"

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the X Impact Checker skill

What this skill tells your AI

The instructions your AI receives, as published by manojbajaj95/claude-gtm-plugin in skills/x-impact-checker/SKILL.md and read by ahel’s review.

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)

FactorMaxScoring Guide
Reply Potential2222: Direct question/debatable claim, 12: Invites response, 4: Statement only
Retweet Potential1616: Actionable insight/surprising fact, 8: Interesting but niche, 0: No share value
Favorite Potential1212: Emotionally resonant/personal story, 6: Useful reference, 0: Low appeal
Quote Potential1010: Strong opinion inviting commentary, 5: Thought-provoking, 0: No quote value

Tier 2: Extended Engagement (25 points)

FactorMaxScoring Guide
Dwell Time66: Long-form/detailed content, 3: Medium depth, 0: Skimmable
Continuous Dwell Time44: Thread/story arc requiring sustained attention, 2: Medium complexity, 0: Quick read
Click Potential55: Compelling link with clear CTA, 3: Link with context, 1: Bare URL, 0: No link
Photo Expand Potential44: Multiple images/visual storytelling, 2: Single image reference, 0: No visual content
Video View Potential33: Long-form video with hook (>5s), 2: Short clip, 0: No video
Quoted Click Potential33: Bold claim inviting verification, 2: Interesting claim, 0: Self-contained

Tier 3: Relationship Building (15 points)

FactorMaxScoring Guide
Profile Click55: Creates author curiosity, 3: Shows expertise, 0: Generic voice
Follow Potential44: Demonstrates ongoing value, 2: Shows potential, 0: One-off content
Share Potential22: General sharing value, 1: Limited appeal, 0: No value
Share via DM22: Personal/relatable "send to friend" content, 1: Somewhat relatable, 0: Generic
Share via Copy Link22: Reference/bookmark worthy, 1: Useful but not evergreen, 0: Ephemeral

Penalties (subtract from total)

RiskRangeTrigger
Not Interested-5 to -15Clickbait, irrelevant content
Mute Risk-5 to -15Repetitive, annoying patterns
Block Risk-10 to -25Offensive, aggressive tone
Report Risk-15 to -30Policy violations, spam signals

Grades

ScoreGrade
90-100S (Exceptional)
75-89A (Strong)
60-74B (Good)
45-59C (Average)
30-44D (Below average)
0-29F (Low potential)

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** |
  1. 📈 Top 5 Priority Improvements: Specific, actionable suggestions across different categories

  2. ✨ 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

LayerStrategy
Real-graphAsk questions; create debate; post when followers are active
SimClustersOne clear topic; use community language; provide niche value
TwHINLead with domain expertise; stay consistent; build topical authority
TweepcredReply to quality accounts; avoid engagement bait; engage deeply

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).

Signals

GitHub stars
99
Forks
30
Last commit
May 2026
Advanced
Catalog kind
skill
Gateway key
x-impact-checker
Source
github.com/manojbajaj95/claude-gtm-plugin