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 AlekseiUL/sprut-agent-kit --skill tweet-writergit clone --depth 1 https://github.com/AlekseiUL/sprut-agent-kitWrote 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/alekseiul/sprut-agent-kit/tweet-writer)<a href="https://agentmods.dev/skills/alekseiul/sprut-agent-kit/tweet-writer"><img src="https://agentmods.dev/badge/skills/alekseiul/sprut-agent-kit/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/alekseiul/sprut-agent-kit/tweet-writer"><img src="https://agentmods.dev/badge/skills/alekseiul/sprut-agent-kit/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 | $0.00014 | $0.02997 |
| Opus 5 | $0.00007 | $0.01499 |
| Sonnet 5 | $0.00003 | $0.00599 |
| Haiku 4.5 | $0.00001 | $0.00300 |
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 10d 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 — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tweet Writer Skill
✍️ Правила текста (стиль владельца)
Обязательно:
- Дефис (-) вместо длинного тире (—). ВСЕГДА
- Сильные глаголы, короткие предложения
- Личный опыт, прямота
Запрещено:
- Длинное тире (—) - заменять на дефис (-)
- "Конечно", "Безусловно", "Стоит отметить", "Является"
- Канцелярит и пассивный залог
Полные правила:
skills/copywriter/SKILL.md
Overview
This skill helps you write viral, persuasive tweets and threads optimized for X's algorithm. It combines proven copywriting frameworks, viral hook formulas, and real-time research to model your content after successful examples in your niche.
Keywords: twitter, X, tweets, threads, viral content, social media, engagement, hooks, copywriting
Process Workflow
Phase 1: Niche Research (CRITICAL)
Before writing ANY tweet, you MUST research viral examples in the user's specific niche.
Research Steps:
- Identify the niche/topic — What is the user writing about?
- Search for viral examples — Use WebSearch to find:
"[niche] viral tweet examples""[niche] twitter thread went viral""[topic] best performing tweets"site:twitter.com OR site:x.com "[niche keyword]" high engagement
- Analyze patterns — Extract:
- Hook styles that worked
- Content structure
- Tone and voice
- Specific numbers/results used
- CTAs that drove engagement
- Document insights — Create a brief analysis before writing
Example Research Prompt:
Searching for: "SaaS founder viral tweets"
"startup advice twitter thread viral"
"tech entrepreneur best tweets engagement"
Phase 2: Tweet Creation
Use the frameworks below to craft content modeled after successful examples.
The X Algorithm (2026)
Understanding what the algorithm rewards is critical:
Engagement Hierarchy (Most to Least Valuable)
- Replies — Most weighted signal
- Quote tweets — High value, shows your content sparks conversation
- Bookmarks — Strong signal of value
- Retweets — Amplification signal
- Likes — Baseline engagement
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.
- 10d ago First seen · 440 lines · 14 tokens per session scan A 1c0df0a37452
tweet-writer is a skill published in the GitHub repository AlekseiUL/sprut-agent-kit (63 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 2,997 once invoked, about $0.0001 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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