# 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. Use when this capability is needed. - Skill: `tomevault-io/twitter-post` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/twitter-post` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/twitter-post/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/twitter-post --- # 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. ``` --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/langchain-ai) — claim your Tome and manage your conversions.