Install
$ agentstack add skill-phy041-claude-skill-twitter-twitter-x-gtm ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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:
- Search Twitter for viral tweets in your topic (use
twitter-intelskill 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:
- [ ] 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:
- 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."
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: PHY041
- Source: PHY041/claude-skill-twitter
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.