AGENT SKILL

x-twitter-growth

X/Twitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X/Twitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.

License 许可
MIT
所属仓库
msm47/Gitskil
最近更新
2026-06-22
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01 前言 Front Matter

name
x-twitter-growth
license
MIT
metadata
{ "author": "Alireza Rezvani", "updated": 1773100800000, "version": "1.0.0", "category": "marketing" }
description
X/Twitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X/Twitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.

02 资源文件 7

03 SKILL.md 正文

X/Twitter Growth Engine

X-specific growth skill. For general social media content across platforms, see social-content. For social strategy and calendar planning, see social-media-manager. This skill goes deep on X.

When to Use This vs Other Skills

Need Use
Write a tweet or thread This skill
Plan content across LinkedIn + X + Instagram social-content
Analyze engagement metrics across platforms social-media-analyzer
Build overall social strategy social-media-manager
X-specific growth, algorithm, competitive intel This skill

Step 1 — Profile Audit

Before any growth work, audit the current X presence. Run scripts/profile_auditor.py with the handle, or manually assess:

Bio Checklist

  • [ ] Clear value proposition in first line (who you help + how)
  • [ ] Specific niche — not "entrepreneur | thinker | builder"
  • [ ] Social proof element (followers, title, metric, brand)
  • [ ] CTA or link (newsletter, product, site)
  • [ ] No hashtags in bio (signals amateur)

Pinned Tweet

  • [ ] Exists and is less than 30 days old
  • [ ] Showcases best work or strongest hook
  • [ ] Has clear CTA (follow, subscribe, read)

Recent Activity (last 30 posts)

  • [ ] Posting frequency: minimum 1x/day, ideal 3-5x/day
  • [ ] Mix of formats: tweets, threads, replies, quotes
  • [ ] Reply ratio: >30% of activity should be replies
  • [ ] Engagement trend: improving, flat, or declining

Run: python3 scripts/profile_auditor.py --handle @username


Step 2 — Competitive Intelligence

Research competitors and successful accounts in your niche using web search.

Process

  1. Search site:x.com "topic" min_faves:100 via Brave to find high-performing content
  2. Identify 5-10 accounts in your niche with strong engagement
  3. For each, analyze: posting frequency, content types, hook patterns, engagement rates
  4. Run: python3 scripts/competitor_analyzer.py --handles @acc1 @acc2 @acc3

What to Extract

  • Hook patterns — How do top posts start? Question? Bold claim? Statistic?
  • Content themes — What 3-5 topics get the most engagement?
  • Format mix — Ratio of tweets vs threads vs replies vs quotes
  • Posting times — When do their best posts go out?
  • Engagement triggers — What makes people reply vs like vs retweet?

Step 3 — Content Creation

Tweet Types (ordered by growth impact)

1. Threads (highest reach, highest follow conversion)

Structure:
- Tweet 1: Hook — must stop the scroll in <7 words
- Tweet 2: Context or promise ("Here's what I learned:")
- Tweets 3-N: One idea per tweet, each standalone-worthy
- Final tweet: Summary + explicit CTA ("Follow @handle for more")
- Reply to tweet 1: Restate hook + "Follow for more [topic]"

Rules:
- 5-12 tweets optimal (under 5 feels thin, over 12 loses people)
- Each tweet should make sense if read alone
- Use line breaks for readability
- No tweet should be a wall of text (3-4 lines max)
- Number the tweets or use "↓" in tweet 1

2. Atomic Tweets (breadth, impression farming)

Formats that work:
- Observation: "[Thing] is underrated. Here's why:"
- Listicle: "10 tools I use daily:\n\n1. X — for Y"
- Contrarian: "Unpopular opinion: [statement]"
- Lesson: "I [did X] for [time]. Biggest lesson:"
- Framework: "[Concept] explained in 30 seconds:"

Rules:
- Under 200 characters gets more engagement
- One idea per tweet
- No links in tweet body (kills reach — put link in reply)
- Question tweets drive replies (algorithm loves replies)

3. Quote Tweets (authority building)

Formula: Original tweet + your unique take
- Add data the original missed
- Provide counterpoint or nuance
- Share personal experience that validates/contradicts
- Never just say "This" or "So true"

4. Replies (network growth, fastest path to visibility)

Strategy:
- Reply to accounts 2-10x your size
- Add genuine value, not "great post!"
- Be first to reply on accounts with large audiences
- Your reply IS your content — make it tweet-worthy
- Controversial/insightful replies get quote-tweeted (free reach)

Run: python3 scripts/tweet_composer.py --type thread --topic "your topic" --audience "your audience"


Step 4 — Algorithm Mechanics

What X rewards (2025-2026)

Signal Weight Action
Replies received Very high Write reply-worthy content (questions, debates)
Time spent reading High Threads, longer tweets with line breaks
Profile visits from tweet High Curiosity gaps, tease expertise
Bookmarks High Tactical, save-worthy content (lists, frameworks)
Retweets/Quotes Medium Shareable insights, bold takes
Likes Low-medium Easy agreement, relatable content
Link clicks Low (penalized) Never put links in tweet body — use reply

What kills reach

  • Links in tweet body (put in first reply instead)
  • Editing tweets within 30 min of posting
  • Posting and immediately going offline (no early engagement)
  • More than 2 hashtags
  • Tagging people who don't engage back
  • Threads with inconsistent quality (one weak tweet tanks the whole thread)

Optimal Posting Cadence

Account size Tweets/day Threads/week Replies/day
< 1K followers 2-3 1-2 10-20
1K-10K 3-5 2-3 5-15
10K-50K 3-7 2-4 5-10
50K+ 2-5 1-3 5-10

Step 5 — Growth Playbook

Week 1-2: Foundation

  1. Optimize bio and pinned tweet (Step 1)
  2. Identify 20 accounts in your niche to engage with daily
  3. Reply 10-20 times per day to larger accounts (genuine value only)
  4. Post 2-3 atomic tweets per day testing different formats
  5. Publish 1 thread

Week 3-4: Pattern Recognition

  1. Review what formats got most engagement
  2. Double down on top 2 content formats
  3. Increase to 3-5 posts per day
  4. Publish 2-3 threads per week
  5. Start quote-tweeting relevant content daily

Month 2+: Scale

  1. Develop 3-5 recurring content series (e.g., "Friday Framework")
  2. Cross-pollinate: repurpose threads as LinkedIn posts, newsletter content
  3. Build reply relationships with 5-10 accounts your size (mutual engagement)
  4. Experiment with spaces/audio if relevant to niche
  5. Run: python3 scripts/growth_tracker.py --handle @username --period 30d

Step 6 — Content Calendar Generation

Run: python3 scripts/content_planner.py --niche "your niche" --frequency 5 --weeks 2

Generates a 2-week posting plan with:

  • Daily tweet topics with hook suggestions
  • Thread outlines (2-3 per week)
  • Reply targets (accounts to engage with)
  • Optimal posting times based on niche

Scripts

Script Purpose
scripts/profile_auditor.py Audit X profile: bio, pinned, activity patterns
scripts/tweet_composer.py Generate tweets/threads with hook patterns
scripts/competitor_analyzer.py Analyze competitor accounts via web search
scripts/content_planner.py Generate weekly/monthly content calendars
scripts/growth_tracker.py Track follower growth and engagement trends

Common Pitfalls

  1. Posting links directly — Always put links in the first reply, never in the tweet body
  2. Thread tweet 1 is weak — If the hook doesn't stop scrolling, nothing else matters
  3. Inconsistent posting — Algorithm rewards daily consistency over occasional bangers
  4. Only broadcasting — Replies and engagement are 50%+ of growth, not just posting
  5. Generic bio — "Helping people do things" tells nobody anything
  6. Copying formats without adapting — What works for tech Twitter doesn't work for marketing Twitter

Related Skills

  • social-content — Multi-platform content creation
  • social-media-manager — Overall social strategy
  • social-media-analyzer — Cross-platform analytics
  • content-production — Long-form content that feeds X threads
  • copywriting — Headline and hook writing techniques

04 同仓库更多 Skills

msm47
msm47/Gitskil

syllabus

Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Triggers whenever a user uploads a syllabus, course outline, or curriculum document and wants supplementary readings. Also triggers on: 'syllabus reading list', 'find papers for my course', 'create a reading list from this syllabus', 'recent research for my class', 'supplementary readings', 'find journal articles for these topics', 'what recent papers cover this material', 'any new research on these course topics', 'update my syllabus with recent papers'. Even casual mentions when a syllabus is attached should trigger this skill.

★ 0
msm47
msm47/Gitskil

research

Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends/sentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Triggers — "research [topic]", "look into [topic]", "what do we know about [topic]", "investigate [topic]", "find me information on [topic]", "do some research on [topic]", "I need to understand [topic]", or any research request that doesn't obviously match a more-specific specialist skill. Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.

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msm47
msm47/Gitskil

pulse

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Triggers: 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]', or any variation requesting multi-source recency intelligence on a topic. Also use for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

★ 0
msm47
msm47/Gitskil

patent

Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Triggers: 'prior art search for [invention]', 'patent search on [topic]', 'freedom to operate analysis', 'FTO for [product]', 'patent landscape for [field]', 'is [invention] novel', 'patents on [topic]', 'competitive patent analysis', 'prior art for litigation', 'patent diligence on [company]'. Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.

★ 0
msm47
msm47/Gitskil

notebooklm

Browser automation skill for controlling Google's NotebookLM. Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio Overview, infographics, slide decks, study guides, briefing docs, mind maps, timelines, FAQs), and creating new notebooks. Triggers on any phrase involving NotebookLM — 'open NotebookLM', 'check my [name] notebook', 'pull info from NotebookLM', 'ask my notebook about X', 'add [source] to NotebookLM', 'create an infographic in NotebookLM', 'use NotebookLM Studio', 'generate a slide deck from my notebook', or any variation where the goal involves NotebookLM. Requires browser automation environment — fails gracefully when unavailable.

★ 0
msm47
msm47/Gitskil

litreview

Academic literature orientation skill that searches papers via Consensus, builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a professionally formatted Word document (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — not a finished review, but an orientation guide that lets a researcher dive in confidently. Triggers: 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'. Do NOT trigger for single one-off paper searches where the user just wants a quick list — that's a plain Consensus search.

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