manojbajaj95/claude-gtm-plugin

x-impact-checker

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. "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…

First seen Feb 19, 2026

Installation

$ npx skills add manojbajaj95/claude-gtm-plugin --skill x-impact-checker

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 95
License LICENSE
Default branch main
Open issues 0
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

LicenseAGPL-3.0 (referencing Twitter's open-source algorithm)

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 12,166 B
  • docs SUMMARY.md 873 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 204 installs

SKILL.md

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)

Factor Max Scoring Guide
Reply Potential 22 22: Direct question/debatable claim, 12: Invites response, 4: Statement only
Retweet Potential 16 16: Actionable insight/surprising fact, 8: Interesting but niche, 0: No share value
Favorite Potential 12 12: Emotionally resonant/personal story, 6: Useful reference, 0: Low appeal
Quote Potential 10 10: Strong opinion inviting commentary, 5: Thought-provoking, 0: No quote value

Tier 2: Extended Engagement (25 points)

Factor Max Scoring Guide
Dwell Time 6 6: Long-form/detailed content, 3: Medium depth, 0: Skimmable
Continuous Dwell Time 4 4: Thread/story arc requiring sustained attention, 2: Medium complexity, 0: Quick read
Click Potential 5 5: Compelling link with clear CTA, 3: Link with context, 1: Bare URL, 0: No link
Photo Expand Potential 4 4: Multiple images/visual storytelling, 2: Single image reference, 0: No visual content
Video View Potential 3 3: Long-form video with hook (>5s), 2: Short clip, 0: No video
Quoted Click Potential 3 3: Bold claim inviting verification, 2: Interesting claim, 0: Self-contained

Tier 3: Relationship Building (15 points)

Factor Max Scoring Guide
Profile Click 5 5: Creates author curiosity, 3: Shows expertise, 0: Generic voice
Follow Potential 4 4: Demonstrates ongoing value, 2: Shows potential, 0: One-off content
Share Potential 2 2: General sharing value, 1: Limited appeal, 0: No value
Share via DM 2 2: Personal/relatable "send to friend" content, 1: Somewhat relatable, 0: Generic
Share via Copy Link 2 2: Reference/bookmark worthy, 1: Useful but not evergreen, 0: Ephemeral

Penalties (subtract from total)

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

Grades

Score Grade
90-100 S (Exceptional)
75-89 A (Strong)
60-74 B (Good)
45-59 C (Average)
30-44 D (Below average)
0-29 F (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)
  1. 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
  1. ✨ 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

Layer Strategy
Real-graph Ask questions; create debate; post when followers are active
SimClusters One clear topic; use community language; provide niche value
TwHIN Lead with domain expertise; stay consistent; build topical authority
Tweepcred Reply 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](references/algorithm-weights.md) for complete weight details from X's open-source algorithm (19-element system).