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
- 最近更新
- 2026-09-27
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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
- 其他.gitignore14 B
- 参考文档references/algorithm-signals.md2.8 KB
- 脚本scripts/competitor_analyzer.py8.1 KB
- 脚本scripts/content_planner.py7.2 KB
- 脚本scripts/growth_tracker.py9.7 KB
- 脚本scripts/profile_auditor.py11.3 KB
- 脚本scripts/tweet_composer.py10.2 KB
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
- Search
site:x.com "topic" min_faves:100via Brave to find high-performing content - Identify 5-10 accounts in your niche with strong engagement
- For each, analyze: posting frequency, content types, hook patterns, engagement rates
- 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
- Optimize bio and pinned tweet (Step 1)
- Identify 20 accounts in your niche to engage with daily
- Reply 10-20 times per day to larger accounts (genuine value only)
- Post 2-3 atomic tweets per day testing different formats
- Publish 1 thread
Week 3-4: Pattern Recognition
- Review what formats got most engagement
- Double down on top 2 content formats
- Increase to 3-5 posts per day
- Publish 2-3 threads per week
- Start quote-tweeting relevant content daily
Month 2+: Scale
- Develop 3-5 recurring content series (e.g., "Friday Framework")
- Cross-pollinate: repurpose threads as LinkedIn posts, newsletter content
- Build reply relationships with 5-10 accounts your size (mutual engagement)
- Experiment with spaces/audio if relevant to niche
- 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
- Posting links directly — Always put links in the first reply, never in the tweet body
- Thread tweet 1 is weak — If the hook doesn't stop scrolling, nothing else matters
- Inconsistent posting — Algorithm rewards daily consistency over occasional bangers
- Only broadcasting — Replies and engagement are 50%+ of growth, not just posting
- Generic bio — "Helping people do things" tells nobody anything
- Copying formats without adapting — What works for tech Twitter doesn't work for marketing Twitter
Related Skills
social-content— Multi-platform content creationsocial-media-manager— Overall social strategysocial-media-analyzer— Cross-platform analyticscontent-production— Long-form content that feeds X threadscopywriting— Headline and hook writing techniques
04 同仓库更多 Skills
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. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.
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. Use when the user requests multi-source recency intelligence on a topic (e.g., '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]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.
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. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). 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.
notebooklm
Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio/Video Overviews, Mind Maps, Reports incl. Briefing Doc/Study Guide/FAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.
litreview
Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a formatted Word (.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' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., '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 use for single one-off paper searches wanting a quick list — that's a plain PubMed/OpenAlex (or Consensus) query.
grants
NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.