Agent skill

X Impact Checker

by manojbajaj95 in manojbajaj95/claude-gtm-plugin

Analyze and optimize X (Twitter) posts for viral potential and reach using heuristics inspired by X's published open-source recommendation architecture.

AGPL-3.0Auto-check passedWriting & Content

Install X Impact Checker

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

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install manojbajaj95/claude-gtm-plugin x-impact-checker --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/manojbajaj95/claude-gtm-plugin.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/x-impact-checker .claude/skills/x-impact-checker && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
x-impact-checker
GitHub stars
105
Token cost
~2.9k tokens
SKILL.md length
1,144 words
Files
2 (incl. references)
Skills in repo
52
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Analyze and optimize X (Twitter) posts for viral potential and reach using heuristics inspired by X's published open-source recommendation architecture.

  • Works in 4 steps: Analyzing post content (in_progress →… → Calculating scores across all elements… → Generating top 5 priority improvements… → …
  • Check if a post will go viral
  • SKILL.md covers Workspace Context, Operating Contract, When to Use and Scoring System (100 points), plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

X Impact Checker is an agent skill from manojbajaj95/claude-gtm-plugin. 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…

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/algorithm-weights.md`).

It sits in Writing & Content, covering Social media posts. It works with X (Twitter). The licence is AGPL-3.0.

When your agent uses it

  • Check if a post will go viral
  • Score a tweet for engagement potential
  • Rewrite a tweet for algorithmic reach
  • Understand why a tweet underperformed

Example prompts

  • “Check if this will go viral”
  • “Make this post buzz”
  • “Will this tweet perform well?”
  • “/x-impact-checker”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  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)

What it can do on your machine

Read from SKILL.md and the folder at commit 0830a46. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

X Impact Checker loads about 2.9k tokens when it runs, and up to ~5.5k if it reads all its reference files. Until then it costs about 189 tokens; SKILL.md has 1,144 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~189
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from manojbajaj95/claude-gtm-plugin at commit 0830a46, republished under its AGPL-3.0 licence (© manojbajaj95). 1,144 words, ~2,875 tokens.

Download SKILL.mdSave it as .claude/skills/x-impact-checker/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
x-impact-checker
description
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"
license
AGPL-3.0 (referencing Twitter's open-source algorithm)

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)
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

Show full SKILL.md (462 more words)Show less
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).

© manojbajaj95, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/x-impact-checker of manojbajaj95/claude-gtm-plugin.

  • SKILL.md
  • references/algorithm-weights.md

Open the folder on GitHubat commit 0830a46

Compare with similar skills

X Impact Checker next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

X Impact Checker compared with similar skills
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Social Contentfreekmurze/dotfiles1k23 repos~2.1kAutomated safety check: PassNone
Typefullyfreekmurze/dotfiles1k1 repos~3.4kAutomated safety check: NotesNone
Changelog Social RecapFlorianBruniaux/claude-code-ultimate-guide6.1k—~1.8kAutomated safety check: NotesCC-BY-SA-4.0
X Mastery Mentoralchaincyf/x-mentor-skill1.2k—~2.3kAutomated safety check: PassMIT

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Works with

Questions about X Impact Checker

What does X Impact Checker do?

Analyze and optimize X (Twitter) posts for viral potential and reach using heuristics inspired by X's published open-source recommendation architecture. X Impact Checker is an agent skill from manojbajaj95/claude-gtm-plugin. Analyze and optimize X (Twitter) posts for viral potential and reach using heuristics inspired by X's published open-source recommendation architecture.

When should I use X Impact Checker?

X Impact Checker fits situations like: check if a post will go viral; score a tweet for engagement potential; rewrite a tweet for algorithmic reach; understand why a tweet underperformed.

How do I install X Impact Checker in Claude Code?

Run `npx skills add manojbajaj95/claude-gtm-plugin --skill x-impact-checker -a claude-code`. Or copy the skill folder (skills/x-impact-checker in manojbajaj95/claude-gtm-plugin) into .claude/skills/x-impact-checker in your project. Claude Code loads it when a task matches its description.

How do I install X Impact Checker in Codex?

Run `npx skills add manojbajaj95/claude-gtm-plugin --skill x-impact-checker -a codex`. Or copy the skill folder (skills/x-impact-checker in manojbajaj95/claude-gtm-plugin) into .agents/skills/x-impact-checker in your project. Codex loads it when a task matches its description.

Can I use X Impact Checker in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add manojbajaj95/claude-gtm-plugin --skill x-impact-checker -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/x-impact-checker, .gemini/skills/x-impact-checker, .github/skills/x-impact-checker and .opencode/skills/x-impact-checker in your project.

What does X Impact Checker need to run?

SKILL.md names no scripts, command-line tools or credentials: X Impact Checker is instructions for the agent only.

Does X Impact Checker access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is X Impact Checker safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does X Impact Checker use?

X Impact Checker is published under the AGPL-3.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does X Impact Checker use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.6k tokens, read only when the agent opens those files.

What are the alternatives to X Impact Checker?

Skills that share tags, products or a category with X Impact Checker: Social (coreyhaines31/marketingskills, 54k stars), Social Content (freekmurze/dotfiles, 1k stars), Typefully (freekmurze/dotfiles, 1k stars) and Changelog Social Recap (FlorianBruniaux/claude-code-ultimate-guide, 6.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains X Impact Checker?

manojbajaj95 (a GitHub user) maintains it in manojbajaj95/claude-gtm-plugin, which has 105 GitHub stars. The repository holds 52 skills in this directory. The repository was last updated on September 18, 2026.

Source: manojbajaj95/claude-gtm-plugin on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.