x-twitter-growth

name	x-twitter-growth
description	This skill should be used when the user asks to "analyze tweets", "grow on Twitter", "build Twitter threads", "optimize X posting schedule", "track follower growth", "improve tweet engagement", or "create a Twitter content strategy".
license	MIT + Commons Clause
metadata	{"version":"1.0.0","author":"borghei","category":"marketing","domain":"social-media","updated":"2026-04-02T00:00:00.000Z","tags":["twitter","x","social-media","engagement","threads","growth-hacking"]}

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

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

Workflow 2: Thread Creation Pipeline

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

Workflow 3: Monthly Growth Review

1. Export analytics data for the period
2. Run growth_tracker.py --period monthly for growth metrics
3. Run tweet_analyzer.py on the same period for content insights
4. Compare engagement rates to prior period
5. 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%