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SKILL verified MIT Self-run

Twitter X Gtm

skill-phy041-claude-skill-twitter-twitter-x-gtm · by PHY041

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.

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Install

$ agentstack add skill-phy041-claude-skill-twitter-twitter-x-gtm

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
5mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

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

| 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:

  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 ( 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:

  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."

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.