Claude-Skills x-twitter-growth

install

source · Clone the upstream repo

git clone https://github.com/borghei/Claude-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/borghei/Claude-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/marketing/x-twitter-growth" ~/.claude/skills/borghei-claude-skills-x-twitter-growth && rm -rf "$T"

manifest: marketing/x-twitter-growth/SKILL.md

source content

X/Twitter Growth Skill

Overview

Production-ready X/Twitter growth toolkit for analyzing tweet performance patterns, structuring optimal threads, and tracking engagement metrics. Designed for creators, marketers, and brand accounts looking to grow audience and engagement systematically through data-driven content decisions.

Quick Start

# Analyze tweet performance patterns from exported data
python scripts/tweet_analyzer.py tweets.csv

# Structure long-form content into optimal Twitter threads
python scripts/thread_builder.py content.txt --target-tweets 8

# Track follower growth, engagement rates, and best posting times
python scripts/growth_tracker.py analytics.csv --period monthly

Tools Overview

Tool	Purpose	Input	Output
`tweet_analyzer.py`	Performance pattern analysis	CSV with tweet data	Engagement patterns + insights
`thread_builder.py`	Thread structuring	Text file or JSON	Formatted thread + hooks
`growth_tracker.py`	Growth & engagement tracking	CSV with analytics data	Growth report + best times

Workflows

Workflow 1: Content Performance Audit

Export tweet data from X Analytics or third-party tool as CSV
Run
```
tweet_analyzer.py
```
to identify top-performing patterns
Identify which content types, formats, and topics drive engagement
Use insights to refine content strategy and posting schedule
Re-audit monthly to track improvement

Workflow 2: Thread Creation Pipeline

Draft long-form content in text or markdown format
Run
```
thread_builder.py
```
to split into optimal thread structure
Review hook tweet (tweet 1) for maximum engagement potential
Add call-to-action and engagement hooks per recommendations
Schedule using identified best posting times from
```
growth_tracker.py
```

Workflow 3: Monthly Growth Review

Export analytics data for the period
Run
```
growth_tracker.py --period monthly
```
for growth metrics
Run
```
tweet_analyzer.py
```
on the same period for content insights
Compare engagement rates to prior period
Identify top 5 tweets and extract replicable patterns

Reference Documentation

See

references/x-growth-playbook.md

for comprehensive strategies covering:

Content format frameworks
Engagement optimization tactics
Thread writing best practices
Algorithm understanding
Growth compounding strategies

Common Patterns

Pattern: Tweet Data CSV Format

tweet_id,text,created_at,impressions,engagements,likes,retweets,replies,type,has_media
T001,"Here's what I learned...",2025-06-15 09:30:00,15000,850,320,95,45,thread_start,no
T002,"Check out this chart",2025-06-14 14:00:00,8500,420,180,35,22,single,yes

Pattern: Thread Content Input

# How I Grew to 50K Followers in 6 Months

The biggest lesson was consistency over virality. Here's the complete breakdown...

[Section 1: Finding Your Niche]
Most creators make the mistake of being too broad. Pick one topic and go deep...

[Section 2: Content Pillars]
I built 3 content pillars that I rotate through each week...

Engagement Rate Benchmarks

Metric	Low	Average	Good	Excellent
Engagement Rate	< 1%	1-3%	3-6%	> 6%
Reply Rate	< 0.1%	0.1-0.5%	0.5-1%	> 1%
Retweet Rate	< 0.2%	0.2-1%	1-3%	> 3%
Thread Completion	< 20%	20-40%	40-60%	> 60%