| name | twitter-x-gtm |
| description | Twitter/X go-to-market content strategy for founders. Use when planning Twitter content strategy, analyzing engagement, identifying accounts to engage with, or creating content. Triggers on "Twitter strategy", "X posting", "founder brand on Twitter", "reach investors on X", "Twitter GTM", or any Twitter/X marketing planning. |
Twitter/X GTM Strategy for Founders
Founder-led personal brand strategy with blunt, sharp, authentic voice. Customize the brand voice section to match your own positioning.
Content Creation Workflow (Must Follow)
Every time creating Twitter/X content, follow this workflow:
Step 1: Research Hot Content
Required Actions:
- Search Twitter for viral tweets in your topic (use
twitter-intel skill or WebSearch)
- Record high-performing tweets':
- Hook structure (first line)
- Thread vs single tweet format
- Engagement patterns (replies vs retweets)
- Tone and punchiness
- Analyze success factors (contrarian takes, specific numbers, relatability)
Step 2: Extract Winning Patterns
| Dimension | What to Extract |
|---|
| Hook Formula | First line that stops scroll |
| Thread Structure | How points are organized |
| Number Usage | Dollar amounts, percentages, timeframes |
| Engagement Bait | What makes people reply |
| Punch/Rhythm | Sentence length and cadence |
Step 3: Adapt with Your Brand Voice
Adaptation Rules:
- Keep the winning hook structure
- Replace with YOUR real stories and data
- Be specific: "$3,000 wasted" > "lost money"
- Add personality: "still cringe", "learned the hard way"
- Keep tweets punchy — short sentences, clear rhythm
- End threads with engagement question
Step 4: Deliver Complete Content
Deliverables Checklist:
Core Positioning (Customize This)
Voice: Blunt, sharp, authentic — "build-in-public meets sharp takes"
Audiences: [Your target audiences — e.g., DTC brand operators, investors/VCs, AI/tech community]
Differentiation: [Your unique angle — what makes your product/perspective different]
Algorithm Essentials (2025)
- Golden Hour: First 60 minutes critical — engagement velocity determines reach
- Comments = 15x likes in algorithmic weight
- Saves are strongest signal
- Threads get 3x engagement vs single tweets
- Freshness decay: 50% reach reduction every 6 hours
- Posts can sustain reach for 2-3 weeks if signals stay strong
Posting Framework
| Element | Spec |
|---|
| Frequency | 3-5 quality tweets/day |
| Threads | 1-2x/week, 7-10 tweets optimal |
| Best times | 9-10 AM EST, 1-3 PM EST |
| Best days | Tuesday, Wednesday, Monday |
| Reply target | 50 quality replies/day (growth phase) |
Content Mix
- 25-30% Build-in-public (metrics, challenges, behind-scenes)
- 25-30% Thought leadership (industry analysis, contrarian takes)
- 15-20% Personal stories (failures, pivots, lessons)
- 15-20% Value/education (tutorials, frameworks)
- 10% max Product promotion
Hook Formulas
Transformation: "6 months ago I was X. Today Y. Here's the playbook:"
Contrarian: "Everyone's building X. Here's why that's actually smart:"
Authority + Promise: "I've done X. Here are the Y patterns:"
Curiosity Gap: "I discovered ONE thing that 10x'd my Z. It has nothing to do with [obvious]:"
Voice Guidelines
Use:
- Specific numbers and real data
- Short, punchy sentences
- Personal stories with lessons
- Honest takes, even uncomfortable ones
Avoid:
- "Revolutionary", "Game-changing", "Seamless"
- Vague claims without data
- Corporate speak
- Excessive hashtags
Conference/Event Content Strategy
Content Cadence
Pre-Event: 2-3 tweets/day
During Event: 3-5 tweets/day (real-time value)
Post-Event: 2-3 tweets/day + 1 thread
Thread Structure (7-10 tweets)
1/ Hook: "Money I wasted at CES..." or "Lessons from my second CES..."
2-6/ Individual points with specifics
7/ Summary/key takeaway
8/ Call for engagement: "What's your experience?"
Tweet Types for Events
| Type | Example | When to Use |
|---|
| Prep tweets | "CES countdown: 9 days. Here's my checklist:" | Pre-event |
| Real-time observations | "Just walked Eureka Park. 3 things that surprised me:" | During |
| Quick tips | "CES pro tip: Comfortable shoes > dress shoes" | Anytime |
| Contrarian takes | "80% of CES booths aren't worth your time. Here's how to find the 20%:" | Post-event |
| Number-driven | "I wasted $1,400 at my first CES. Here's the breakdown:" | Post-event |
Input/Output Examples
Example 1: Single Tweet Request
User Input:
"Help me write a tweet about CES"
Expected Output:
Main Tweet:
"Lesson from CES: The founders who got meetings weren't the loudest. They were the ones who booked 6 weeks early.
90% of Brella meetings are scheduled before the event starts. I learned this the hard way last year."
Self-reply (post 10 min later):
"The real flex at CES isn't your booth. It's your calendar."
Schedule: 9:00 AM EST Tuesday
Character count: 248 OK
Example 2: Thread Request
User Input:
"Write a thread about AI creative tools"
Expected Output:
1/ Most AI creative tools have a dirty secret.
They make everything look the same.
Here's what I learned testing 15+ tools for brand creative:
2/ The problem isn't the AI.
It's that these tools optimize for "good enough" — not "on-brand."
3/ After 6 months of testing:
- Tool A: Great art, zero brand memory
- Tool B: Generic copy every time
- Tool C: Templates that sound like everyone else
4/ The missing piece: Brand memory.
Not a new term. It's how the best brand teams already work — they have a "brand bible" in their heads.
5/ What if AI could learn that bible?
That's what we're building.
6/ Early results:
- 10 hours saved per week
- Creative that actually passes brand review first time
- No more "make it more on-brand" feedback loops
7/ The shift happening now:
From: AI that generates content
To: AI that generates YOUR content
Who else is tired of generic AI output?
Example 3: Build-in-Public Update
User Input:
"We just shipped a new feature, help me write a tweet"
Response Pattern:
- Ask: "What feature? Who benefits? One metric if available?"
- Then generate tweet with:
- What shipped (specific)
- Why it matters (user benefit)
- One proof point (number or before/after)
- No hype words
Example Output:
"Shipped: Auto-brand-check for ad creative.
Before: 3 rounds of revision to pass brand review.
After: 90% first-time approval rate.
The surprising part: Most rejections weren't about design. They were about tone."
