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 sanky369/vibe-building-skills --skill tweet-writergit clone --depth 1 https://github.com/sanky369/vibe-building-skillsWrote 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/sanky369/vibe-building-skills/tweet-writer)<a href="https://agentmods.dev/skills/sanky369/vibe-building-skills/tweet-writer"><img src="https://agentmods.dev/badge/skills/sanky369/vibe-building-skills/tweet-writer/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/sanky369/vibe-building-skills/tweet-writer"><img src="https://agentmods.dev/badge/skills/sanky369/vibe-building-skills/tweet-writer.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 | $0.00133 | $0.01938 |
| Opus 5 | $0.00067 | $0.00969 |
| Sonnet 5 | $0.00027 | $0.00388 |
| Haiku 4.5 | $0.00013 | $0.00194 |
Grade A, and why
tweet-writer 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 12d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-voicefirst — hooks in someone else's voice read as spam.
Intake
Ask in one batch, only what's missing:
- Topic and the core insight — what's the one thing this tweet says?
- Niche and audience — who follows (or should follow) this account?
- Goal — replies/engagement, followers, or clicks/conversions? (Changes the CTA and format.)
- 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.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 134 lines · 133 tokens per session scan A 155963a560a1
tweet-writer is a skill published in the GitHub repository sanky369/vibe-building-skills (30 stars, last pushed 2mo ago), licensed MIT. It adds 133 tokens to every session and 1,938 once invoked, about $0.0007 per session on Opus 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 2026-08-30.
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