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twitter-post

// 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.

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updated:March 3, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
nametwitter-post
descriptionWrite 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.