Skill: phy041/phy-twitter-x-gtm (version 1.0.2)

--- name: twitter-x-gtm description: Twitter/X go-to-market strategy for founders and product builders. Use when planning Twitter content strategy, analyzing engagement, identifying accounts to engage with, or creating content for Western audiences and investors. Triggers on "Twitter strategy", "X posting", "founder brand on Twitter", "reach investors on X", "DTC Twitter", or any Twitter/X marketing planning. homepage: https://canlah.ai metadata: {"openclaw": {"emoji": "𝕏", "os": ["darwin", "linux"]}} --- # Twitter/X GTM Strategy Founder-led personal brand strategy targeting DTC brands and investors with blunt, sharp, authentic voice. --- ## 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 WebSearch or browser) 2. Record high-performing tweets': - Hook structure (first line) - Thread vs single tweet format - Engagement patterns (replies vs retweets) - Tone and punchiness 3. Analyze success factors (contrarian takes, specific numbers, relatability) **Search Examples:** ``` Twitter [topic] viral thread site:twitter.com founder [topic] lessons [topic] "here's what I learned" site:x.com ``` ### 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 **Brand Voice:** - Blunt, sharp, authentic - "Build-in-public meets sharp takes" - Anti-AI-slop — real human voice - Specific numbers, no vague claims **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 **Voice:** Blunt, sharp, authentic — "build-in-public meets sharp takes" **Audiences:** DTC brand operators, investors/VCs, AI/tech community **Differentiation:** Anti-AI-slop positioning — real human voice with builder credibility ## 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:** - "AI that actually learns your brand voice" - "Saved our team 10 hours last week" - "Here's what I learned building [your product]" **Avoid:** - "Revolutionary AI platform" - "Game-changing technology" - "Seamless integration" ## Conference/Event Content Strategy (CES/MWC etc.) ### 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 | ### What Works on Twitter for Events - Specific numbers and dollar amounts - Contrarian observations (what others are missing) - Quick, actionable tips - Behind-the-scenes honesty - Threads with clear structure ### What to Avoid - "So excited to be at CES!" (zero value) - Reposting official announcements - Product pitches disguised as insights - Vague observations without specifics --- ## Input/Output Examples ### Example 1: Single Tweet Request **User Input:** > "帮我写一条关于 CES 的 tweet" **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 ✓ ``` ### Example 2: Thread Request **User Input:** > "写一个关于 AI creative tools 的 thread" **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: - Midjourney: Great art, zero brand memory - ChatGPT: Generic copy every time - Jasper: 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 at [Your Company]. 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? --- Thread length: 7 tweets ✓ Hook formula: "dirty secret" (curiosity gap) ✓ Includes numbers: 15+ tools, 6 months, 10 hours ✓ CTA: Question at end ✓ ``` ### Example 3: Build-in-Public Update **User Input:** > "我们刚 ship 了一个新功能，帮我写个 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." ``` --- ## Author **[Canlah AI](https://canlah.ai)** — Run performance marketing without breaking your brand. - GitHub: [github.com/PHY041](https://github.com/PHY041) - All Skills: [clawhub.ai/PHY041](https://clawhub.ai/PHY041)


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