langchain-ai/twitter-post
agents/deep_agent/skills/twitter-post/SKILL.md
Write a Twitter/X post or thread based on research findings or a given topic. Use this skill when asked to create tweets, X posts, or social media threads.
$ install --global
skillsauthnpx skillsauth add langchain-ai/langgraph-101 twitter-postInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
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SemgrepStatic code analysis for vulnerabilities
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Snyk (dep)Open source security scanning
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Socket.devSupply chain security analysis
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VirusTotalMulti-engine malware detection
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CrowdStrikeAdvanced threat intelligence
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OWASP Dep-Check
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Last scanned: Apr 16, 2026, 6:25 AM3.9s1 file scanned
SKILL.md
- name:
- twitter-post
- description:
- Write a Twitter/X post or thread based on research findings or a given topic. Use this skill when asked to create tweets, X posts, or social media threads.
Twitter/X Post Skill
Single Tweet Format
- Maximum 280 characters
- Lead with the most compelling point
- Use numbers or data when possible
- End with a link placeholder or call-to-action
- 1-2 hashtags max (optional)
Thread Format (for longer content)
- Tweet 1: Hook + preview of what's coming (e.g., "A thread on X:" or "Here's what I found:")
- Tweets 2-N: One idea per tweet, numbered (1/, 2/, 3/)
- Final tweet: Summary + call-to-action + link
- Keep each tweet self-contained (people share individual tweets)
- 4-8 tweets is the sweet spot for engagement
Tone
- Concise and punchy
- Opinionated takes perform better than neutral summaries
- Use plain language -- no corporate speak
- Contrarian or surprising angles get more engagement
Tips
- Front-load the value (no throat-clearing or preambles)
- Use line breaks within tweets for readability
- Avoid hashtags in threads (they look spammy) -- save them for single tweets
- Numbers and lists catch the eye in a feed
Example Single Tweet
AI agents that manage their context window well outperform those with 10x more tools.
The secret isn't more capabilities -- it's smarter context engineering.
Example Thread
Thread: What makes AI agents actually work in production? 🧵
1/ It's not the model size. It's context management.
The best agents treat their context window like RAM -- offloading to filesystem, summarizing aggressively, loading info on demand.
2/ Subagents are the key to scaling.
Instead of one agent doing everything, delegate to specialists. The main agent only sees the summary, not 50 intermediate tool calls.
3/ Skills > giant system prompts.
Progressive disclosure: load detailed instructions only when the task needs them. Your agent's prompt stays clean until it matters.
4/ Memory needs structure.
Semantic (facts), episodic (experiences), procedural (rules) -- route them to different backends so they persist appropriately.
5/ The takeaway: the best agent architectures are about information flow, not raw capability.
What patterns are you using? Reply with your favorite agent architecture trick.
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