Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PHY041/claude-skill-twitter --skill twitter-x-gtmgit clone --depth 1 https://github.com/PHY041/claude-skill-twitterWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/phy041/claude-skill-twitter/twitter-x-gtm)<a href="https://agentmods.dev/skills/phy041/claude-skill-twitter/twitter-x-gtm"><img src="https://agentmods.dev/badge/skills/phy041/claude-skill-twitter/twitter-x-gtm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/phy041/claude-skill-twitter/twitter-x-gtm"><img src="https://agentmods.dev/badge/skills/phy041/claude-skill-twitter/twitter-x-gtm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.01757 |
| Opus 5.5 | $0.00029 | $0.00703 |
| Sonnet 5.5 | $0.00014 | $0.00351 |
| Haiku 4.5 | $0.00007 | $0.00176 |
Grade A, and why
twitter-x-gtm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 (<=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]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 230 lines · 72 tokens per session scan A 641dceb417d1
twitter-x-gtm is a skill published in the GitHub repository PHY041/claude-skill-twitter (5 stars, last pushed 8mo ago), licensed MIT. It adds 72 tokens to every session and 1,757 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
twitter-x-gtm compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| twitter-x-gtmPHY041/claude-skill-twitter | Skill | 5 | 8mo ago | MIT | A | 72 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |