twitter-x-gtm（推特 x GTM）

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:

1. Search Twitter for viral tweets in your topic (use twitter-intel skill or WebSearch)
2. Record high-performing tweets':

- Hook structure (first line) - Thread vs single tweet format - Engagement patterns (replies vs retweets) - Tone and punchiness

1. Analyze success factors (contrarian takes, specific numbers, relatability)

Step 2: Extract Winning Patterns

Step 3: Adapt with Your Brand Voice

Adaptation Rules:

1. Keep the winning hook structure
2. Replace with YOUR real stories and data
3. Be specific: "$3,000 wasted" > "lost money"
4. Add personality: "still cringe", "learned the hard way"
5. Keep tweets punchy — short sentences, clear rhythm
6. End threads with engagement question

Step 4: Deliver Complete Content

Deliverables Checklist:

• Main tweet (hook + value + CTA)
• Thread structure if applicable (7-10 tweets)
• Character count check (<=280 per tweet)
• Reply templates for common responses
• Scheduling times (9 AM, 1 PM, 3 PM EST)
• Self-reply tip to add (boost engagement)

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

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

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:

1. Ask: "What feature? Who benefits? One metric if available?"
2. 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."