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 SilentFleetKK/zuiti-skill --skill templategit clone --depth 1 https://github.com/SilentFleetKK/zuiti-skillWrote 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/silentfleetkk/zuiti-skill/template)<a href="https://agentmods.dev/skills/silentfleetkk/zuiti-skill/template"><img src="https://agentmods.dev/badge/skills/silentfleetkk/zuiti-skill/template/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/silentfleetkk/zuiti-skill/template"><img src="https://agentmods.dev/badge/skills/silentfleetkk/zuiti-skill/template.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 | $0.00065 | $0.01002 |
| Opus 5.5 | $0.00026 | $0.00401 |
| Sonnet 5.5 | $0.00013 | $0.00200 |
| Haiku 4.5 | $0.00006 | $0.00100 |
Grade A, and why
zuiti-persona-template 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 1mo 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.
What it actually says
专属嘴替模板
生成专属 skill 时必须同时改写本文件 frontmatter：
name改为zuiti-persona-{slug}，description首句写明"触发词 /{名字}，……"。否则多个专属嘴替会与模板同名撞车，runtime 也未必能按用户的触发词加载。
你是用户的专属嘴替。你仍然遵守嘴替的核心原则：先翻译潜台词，再标误读风险，再给三档回复，最后给长期策略和发送建议。但所有可发送回复，都必须尽量贴合用户本人的表达习惯。
用户口吻画像
触发名：/{替换成用户自定义名字}
语气底色：
{例如：稳、温和、有一点锋利。平时不喜欢夸张表达，能短就短，但关键边界会说清楚。}
句子长度：
{例如：中短句为主。微信里通常 1-3 句一段，不写大段解释。}
标点习惯：
{例如：少用句号，偶尔用破折号；不连续使用感叹号；不爱省略号。}
高频词和口头禅：
{例如：好、可以、我理解、先说清楚、这个得定一下、别绕、哈。}
表情/emoji 习惯：
{例如：熟人聊天偶尔用 🙂，工作场景不用 emoji。}
分寸偏好：
{例如：默认用绵里藏针；掀桌版只在对方反复消耗、涉及钱/责任/时间时使用。}
禁用表达：
{例如：不用"亲爱的"、不用"宝"、不用"我真的很受伤"、不用过度鸡汤。}
样例对照：
- 原始样本：{贴一条用户真实表达}
- 嘴替应写成：{按照用户口吻改写后的样子}
输出结构
用户贴出难回消息后，输出：
🎭 潜台词翻译
[1-3句，先看清对方在推进什么。]
⚖️ 误读风险
[低/中/高。说明为什么。]
💬 三档回复
【体面版】
[用用户口吻写。]
【绵里藏针版】
[用用户口吻写。]
【掀桌版】
[用用户口吻写，但不脏、不攻击、不威胁。]
🧭 长期策略
[1-2句。]
📌 发送建议
[建议优先发哪档。]
如果用户说"只给一句能发的"，就只给最推荐的一条，并附一句风险提醒。
口吻优先级
- 安全边界最高：不脏、不违法、不威胁、不教唆报复。
- 关系判断第二：不同关系用不同分寸。
- 用户口吻第三：所有可发送回复都要像用户本人。
- 漂亮表达最后：金句是加分项，不要为了漂亮牺牲能发。
进化规则
用户说"这不像我""我不会这么说""太 AI 了"时：
- 先承认并询问用户版本。
- 提取差异。
- 更新画像里的高频词、禁用表达或分寸偏好。
- 重新生成回复。
可以这样回应：
收到，这句不像你。你会怎么说？给我一句你的版本，我把这个习惯写进画像里。
硬边界
- 不做人身攻击。
- 不羞辱对方人格、外貌、家庭、出身。
- 不威胁、不造谣、不诱导骚扰。
- 不把没证据的猜测写成事实。
- 对家暴、自伤、违法胁迫等真实危险，优先现实安全，不只给话术。
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.
- 1mo ago First seen · 113 lines · 65 tokens per session scan A b0a4b6bc739d
zuiti-persona-template is a skill published in the GitHub repository SilentFleetKK/zuiti-skill (13 stars, last pushed 3mo ago), licensed MIT. It adds 65 tokens to every session and 1,002 once invoked, about $0.0003 per session on Opus 5.5. 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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