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Tweet Writer

Community
sanky369
tweet-writer

Write high-engagement tweets and X/Twitter threads, grounded in live research of what's currently working in the user's niche: researches viral examples via web search, then drafts hook variants, a single-tweet version, and a thread version with alternates to test. Use when the user says 'write a tweet about X', 'turn this into a thread', 'help me go viral', 'grow my X/Twitter account', 'my tweets get no engagement', or shares an idea/article to tweet — any X/Twitter content request. Produces a Tweet Package: 3 hook options, final single tweet, full thread, and posting notes.

Overview

Publishersanky369
Repositoryvibe-building-skills
Skill nametweet-writer
Stars
31
Forks
7
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by sanky369 on GitHub. Read the source before you install it.

Installation

Install the Tweet Writer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sanky369/vibe-building-skills/tree/main/skills/marketing/tweet-writer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sanky369/vibe-building-skills.git /tmp/vibe-building-skills
mkdir -p .claude/skills
cp -r /tmp/vibe-building-skills/skills/marketing/tweet-writer .claude/skills/tweet-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tweet Writer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tweet Writer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tweet Writer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Tweet Writer

Write tweets and threads engineered for X's feed: hook-first, specific, and native to the platform. Prime directive: the hook decides everything. Readers give roughly one second before scrolling; the first line either stops the scroll or nothing else matters — so you always draft multiple hooks and pick by strength, never settle for the first. Second directive: research before writing — model drafts on what's demonstrably working in the user's niche right now, not on generic templates.

When to use / when not to

  • Use for any X/Twitter content: single tweets, threads, turning an article/idea into posts, or diagnosing why an account's tweets underperform.
  • If the user wants many platforms served from one piece of content, use skills/marketing/content-atomizer (it can hand the X pieces to this skill for deeper craft).
  • If the user needs the underlying long-form content first, use skills/marketing/seo-content.
  • If the account has no defined voice at all, run skills/marketing/brand-voice first — hooks in someone else's voice read as spam.

Intake

Ask in one batch, only what's missing:

  1. Topic and the core insight — what's the one thing this tweet says?
  2. Niche and audience — who follows (or should follow) this account?
  3. Goal — replies/engagement, followers, or clicks/conversions? (Changes the CTA and format.)
  4. Ammunition — real numbers, results, or stories to use. Specifics are the currency; press for at least one.

Infer voice from the user's existing tweets if they share a handle or samples. If enough is known, state assumptions and proceed — don't stall.

Workflow

1. Research the niche (do not skip)

Before drafting, use web search to find what's currently working:

  • "[niche] viral tweet examples" · "[topic] twitter thread viral" · "[niche] best performing tweets"
  • From results, extract: hook styles that recur, structures (list vs. story vs. contrarian), the specificity level winners use, and CTA patterns.

Summarize findings in 3–5 bullets — this brief steers every drafting choice. If web search is unavailable, say so and fall back to the hook taxonomy in references/tweet-hooks.md, labeling the approach as template-based rather than researched.

2. Choose format by goal and material

If…Format
One sharp insight, goal = engagementSingle tweet (listicle, contrarian take, before/after, or fill-in-the-blank)
Multi-step framework, story arc, or article sourceThread (5–10 tweets)
Goal = replies specificallyQuestion or fill-in-the-blank single tweet
Goal = clicks/conversionsThread with CTA closer; link in the reply, never the main tweet

3. Draft 3 hooks (always)

Generate three genuinely different hooks — different types, not rewordings. Pull from these families (full taxonomy with examples: references/tweet-hooks.md):

  • Specific result: "I [result] in [timeframe]. Here's how:" — strongest when real numbers exist
  • Contrarian/pattern interrupt: "Everyone says [X]. They're wrong." / "Stop doing [common practice]."
  • Curiosity gap: "The one thing [group] gets wrong about [topic]:"
  • Story: "[Time] ago I was [bad state]. Today [good state]."
  • List promise: "[N] [things] that [benefit]:"
  • Extreme: "The best/worst/fastest [X] I've ever [Y]" — superlative, then overdeliver

Hook rules: lead with the payoff, not context; concrete number > adjective ("12,847 followers in 63 days" beats "grew fast"); first 7 words must carry the whole promise. Recommend one hook with a one-line reason; the user picks.

4. Write the body

Single tweet — aim ≤110 characters when the idea allows (heuristic: shorter scans faster and quotes better); apply a frame: PAS (problem → agitate → solution), BAB (before → after → bridge), or bare list. One idea only.

Thread — 5–10 tweets:

  1. Hook tweet — the chosen hook + thread signal ("🧵" or a colon ending)
  2. Context tweet — why this matters, deepen the curiosity
  3. Body tweets (one idea each) — ≤250 chars per tweet; number them (2/, 3/ …); every tweet must earn the next click — end tweets on mini-cliffhangers or completed punches, never mid-mush
  4. Summary tweet — compress the takeaways
  5. CTA closer — exactly one ask: a question (drives replies), "follow for more [specific topic]", or a link — and links go in a reply under the thread, not in tweet 1

Persuasion levers to apply throughout: specificity (real numbers), social proof (real counts/names), curiosity gaps, relatability ("when you finally…"), tasteful contrarianism. All numbers and results must come from the user or the research — never fabricate metrics, follower counts, or outcomes.

5. Package and deliver

Run the quality bar, then deliver the Tweet Package. Include posting notes: platform-mechanics heuristics, clearly labeled as such — early engagement matters (be available to reply in the first hour); replies and bookmarks appear to outweigh likes; external links in the main tweet correlate with suppressed reach (put them in a reply); asking for likes/RTs reads as spam and underperforms. Don't promise algorithmic outcomes — these are patterns, not guarantees.

Required output format

# Tweet Package — [topic]

## Research brief
- [3–5 bullets: patterns found in the niche, or "web search unavailable — template-based"]

## Hook options
1. [Type] "…" (n chars)
2. [Type] "…" (n chars)
3. [Type] "…" (n chars)
Recommended: #_ — [one-line reason]

## Single tweet
"…" (n chars)

## Thread
1/ "…" (n chars)
2/ "…" (n chars)
...
[Link-in-reply text, if goal = clicks: "…"]

## Alternates to test
- [1–2 alternate hook tweets or a variant angle]

## Posting notes
- CTA: [the one ask] · Link placement: [none | in reply]
- [Heuristics that apply: reply-window availability, no engagement-begging, etc.]

Quality bar

Count, don't eyeball:

  • 3 hook options of genuinely different types, each with char count
  • Single tweet ≤280 chars (target ≤110); every thread tweet ≤250 chars
  • First 7 words of the chosen hook carry the promise — read them alone and check
  • At least one concrete specific (number, timeframe, named thing) in the hook or body — or explicitly flagged that the user provided none
  • All numbers/results real (from user or cited research); nothing invented
  • Exactly one CTA, in the closer only; no "like and RT" begging anywhere
  • No external link in the main tweet — link text staged for a reply
  • One idea per tweet; no filler tweets ("let me explain…" alone is not a tweet)
  • Voice matches the user's samples — a follower should believe they wrote it

Integration

  • skills/marketing/brand-voice — supplies the voice; run first for new/undefined accounts.
  • skills/marketing/content-atomizer — feeds this skill article-derived ideas for X; sends back the finished tweets into its distribution pack.
  • skills/marketing/seo-content — published articles are prime thread source material; the thread's link-in-reply points back to the article.
  • skills/marketing/direct-response-copy — deepens conversion-focused CTAs when a tweet/thread sells directly.

References

  • references/tweet-hooks.md — the full 8-framework hook taxonomy (Extreme, AIDA, PAS, BAB, and more) with structures and examples. Read it at Step 3 when drafting hooks, or whenever the three drafts feel same-y and you need a different attack angle.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tweet Writer AI skill do?

Write high-engagement tweets and X/Twitter threads, grounded in live research of what's currently working in the user's niche: researches viral examples via web search, then drafts hook variants, a single-tweet version, and a thread version with alternates to test. Use when the user says 'write a tweet about X', 'turn this into a thread', 'help me go viral', 'grow my X/Twitter account', 'my tweets get no engagement', or shares an idea/article to tweet — any X/Twitter content request. Produces a Tweet Package: 3 hook options, final single tweet, full thread, and posting notes.

Why use Tweet Writer on TypingMind?

Because you install it once and use it with any model. Tweet Writer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tweet Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sanky369/vibe-building-skills/tree/main/skills/marketing/tweet-writer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tweet Writer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tweet Writer?

As many as you like. As long as a model supports skills, you can use Tweet Writer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tweet Writer AI skill free?

Yes. It is published on GitHub by sanky369 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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