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 o...

Security Scan

The skill's code and runtime instructions are coherent with an X/Twitter growth tool: it performs local analysis, content generation, and stores local snapshots, and it does not request credentials or reach out to external endpoints.

Purpose & Capability

Name/description (X/Twitter growth, content creation, competitor research) match the included scripts: profile audit, competitor analysis, content planning, tracker, and tweet composer. Minor mismatch: SKILL.md and some docstrings mention 'scraping public info via web search' and instruct using the Brave browser, but the scripts themselves do not implement automated web requests or scraping — they expect either user-provided data or a JSON import for competitor/profile info. This is likely a documentation vs implementation inconsistency, not an indication of hidden capabilities.

Instruction Scope

Runtime instructions tell the agent/user to run the provided Python scripts and to perform web searches (e.g., site:x.com via Brave). The scripts operate on local data, accept JSON imports, and the growth_tracker writes snapshot lines to a local .growth-data directory. The instructions do not ask for or read system credentials or unrelated config paths. The only scope caveat: SKILL.md suggests browsing/scraping; the actual scripts require manual data collection or JSON imports, so following the SKILL.md may lead the user/agent to perform web browsing outside the script's code.

Install Mechanism

No install spec — code is delivered as plain Python scripts and a README-like SKILL.md. Nothing is downloaded or executed automatically beyond running the included scripts locally. This is low-risk from an install mechanism perspective.

Credentials

The skill requires no environment variables, credentials, or external tokens. It writes local data (./.growth-data) for the growth_tracker; that local storage is proportionate to tracking functionality but users should be aware of where data is stored.

Persistence & Privilege

Flags show always:false and the skill is user-invocable. It does not request permanent inclusion nor attempt to modify other skills or system-wide agent settings. Its persistent effect is limited to local files it creates (the .growth-data directory).

Assessment

This skill appears to do what it says: local profile audits, competitor analysis (from user-supplied data), content planning, and tweet drafting. Before running it: 1) Note the documentation suggests using a browser/search to collect competitor posts — the scripts themselves expect you to provide that data (via JSON or flags). Don't assume the tool will fetch data for you. 2) The growth_tracker will create and append JSONL files under a .growth-data directory (one level above the scripts); inspect those files if they will contain sensitive metrics. 3) There are no credential requests or network calls in the provided code, but if you plan to extend the skill to fetch data automatically, review any added networking code carefully. 4) Run the scripts in a controlled environment (or inspect the code) if you plan to process real account data — this reduces accidental data leakage or accidental uploads when you later integrate browsing/automation tools.

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

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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

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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?

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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"

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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

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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

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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

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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