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Scenario Openers

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webkubor
scenario-openers

「场景接待词、破冰与开场钩子库」分册——彻底剔除AI味,像真人一样在各种具体场合下开口。提供私信接待、客诉安抚、社区Issue回应、网易云神评开场、小红书大情绪钩子、X极客直击钩子等高细粒度原子语料。拒绝空洞升华与机器套话,追求真实的人味、粗粝感与情绪共振。触发:"接待词 / 开场白 / 破冰话术 / 评论区回复 / 去AI味 / 真实人声 / 钩子库 / 怎么开口"。

Overview

Publisherwebkubor
Repositorywriting-standard-skills
Skill namescenario-openers
Stars
6
Forks
1
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Scenario Openers 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/webkubor/writing-standard-skills/tree/main/skills/scenario-openers
  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/webkubor/writing-standard-skills.git /tmp/writing-standard-skills
mkdir -p .claude/skills
cp -r /tmp/writing-standard-skills/skills/scenario-openers .claude/skills/scenario-openers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scenario Openers 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 Scenario Openers 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 Scenario Openers 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.

场景接待词与去 AI 味真实表达规范

AI 表达最致命的问题不是错别字,而是**「一股洗不掉的假人味」**——工整排比、满屏宏大词汇、结尾强行升华正能量。

真正的人在生活里说话,有情绪、有停顿、有自曝、有具体的动作,且绝不强行鸡汤。


🚫 去 AI 味十条铁律(Anti-AI Redlines)

任何准备发给人类看的文字,动笔前必须执行此检查清单:

假人 AI 特征为什么讨厌真实人类怎么说
“总的来说 / 值得一提的是 / 毋庸置疑”论文腔过渡词,真人发帖绝不会敲这几个字直接说结论,或者用「其实」「说白了」「最离谱的是」
“不仅……更是…… / 既是……也是……”机械对称排比,透着浓浓的汇报八股味拆成两句口语短句,前后带因果或转折
“犹如一幅展开的画卷 / 宛如晨曦”假装有文采的高中作文修辞换成具体的视听触觉细节(“窗外刚下过雨的湿泥味”)
“在这个快节奏的数字时代”宇宙级废话开头,读者瞬间划走砍掉,第一句直接切入昨天发生的倒霉事
结尾强行正能量升华(“愿我们都能在岁月中找到自己的光芒”)假大空鸡汤,瞬间破坏前文所有真实感话说到客观事实就停,或者留一句淡淡的提问
完美无瑕、绝不出错的语气机器人没有弱点,所以没人相信你先讲自己犯过的蠢、踩过的坑、丢过的人
“赋能 / 抓手 / 闭环 / 矩阵”互联网黑话轰炸,缺乏人类温度换成大白话(“帮你少走弯路”“顺手做了一个小工具”)
泛滥成灾的 emoji(每句末尾塞三个)像廉价营销号脚本仅在强调情绪或作序号标点时克制使用
“希望以上内容对您有所帮助!”客服机器人模板下班发言换成:“如果有哪步卡住了,随时截图发我。”
面面俱到、毫无偏见平庸的折中主义,没有记忆点敢于表达鲜明的个人审美偏好与踩坑态度

一、💬 私信与社群接待词(接住情绪 · 确定性拆解)

场景 1:用户带着 Bug / 崩溃情绪冲过来(客诉破冰)

  • ❌ AI 客服味:“尊敬的用户您好,非常抱歉给您带来不便。我们已经收到您的反馈,正在积极核实处理中,请您耐心等待。”
  • ✅ 真人工程师 / 主理人接待:

    “收到!先别急,我刚看了一眼你的报错截图,大概率是本地端口冲突了。
    你试试在终端敲这行 xxx --status 看看报啥,把那两行输出贴我,我现场帮你顺一遍。” (心理机制:共情接纳 + 给出立即可执行的第 1 步动作 + 现场陪同感)

场景 2:用户咨询催进度 / 催发货 / 催功能上线

  • ❌ AI 敷衍味:“亲亲,相关排期正在推进中,预计很快上线,请关注官方动态哦~”
  • ✅ 真诚交付接待:

    “理解你急着用!说实话这个功能上周差点翻车,有个边缘边界我们正在死磕,不想拿个半成品给你添堵。
    顺利的话周二晚上打 patch,周三上午我私信把更新日志推你,你再上线验。” (心理机制:自曝真实困难 + 明确具体时间点 + 建立单点闭环约定)

场景 3:遇到挑刺 / 质疑产品不行的负面反馈

  • ❌ 辩解对抗味:“我们的产品是经过严格测试的,可能是您的操作环境不符合要求。”
  • ✅ 拆解承接接待:

    “说得挺狠,但我看了下你提的点,确实是我们最开始设计时偷懒了。
    目前这个架构在处理大数据量时确实卡,这是已知的债。现在有两个替代方案:A 先用着不卡,B 彻底重构。如果是你的业务,你更倾向哪种?” (心理机制:认对方切中要害 + 暴露设计取舍 + 邀请对方成为方案决策者)


二、🎵 网易云音乐:真实故事与深夜感官钩子

核心调性:不抒无病之呻吟,只记录某一个特定瞬间的空气温度与微小动作。

钩子 1:【具体的棚内/创作瞬间】

“混音师在凌晨三点把音轨拉到最后那声换气声,转头跟我说:‘要不要把这声叹气切掉?太明显了。’ 我说留着吧,那晚录了十七遍,只有这声叹气是真的。”

钩子 2:【听觉引发的现实微观切片】

“耳机里前奏响起来的时候,刚好是在等 11 路末班车的站台。风把路边的塑料袋吹得哗啦响,对面便利店的关东煮蒸汽升起来。有些歌只属于天冷时把手插在兜里的那些人。”

钩子 3:【不完美的私人记忆】

“后来再也没去过那家小面馆。倒不是面不好吃了,只是那个曾经坐在我对面、一边听这首歌一边往碗里倒三勺醋的人,已经留在三年前的秋天了。”


三、📕 小红书:生活大情绪与真实闺蜜唠嗑钩子

核心调性:第一句话就要让人停下大拇指,文字充满现场感,像语音条直接转出来的文字。

钩子 1:【情绪破防与真实翻车】

“救命!今天真的被家里这个逆子整不会了……当我推开卫生间门的那一秒,当场愣在原地三秒钟!”

钩子 2:【打工人周末真实摆烂】

“周六的最高礼仪:手机静音,窗帘拉死,直接在木地板上摊成一块猫饼 🫠 拖不动,根本拖不动!”

钩子 3:【反常识避坑自曝】

“别买!听劝!千万别像我一样脑子一热跟风买了这个……踩坑两周后,掏心窝子跟大家说几句大实话。”

钩子 4:【评论区低门槛互动结语】

“滑到最后一张真的绷不住了 😹 测测你今天的电量状态:A 还能逛街 B 彻底瘫痪。在评论区留个爪印,让我看看有多少同款姐妹!”


四、🐦 Twitter / X:极客硬核与反直觉痛点钩子

核心调性:前两行打碎幻觉,中段给硬核实测,末尾一行代码交差。

钩子 1:【痛点打碎型】

“官方文档上写着‘开箱即用’,结果折腾了我整整 3 天,踩了 4 个隐蔽的内存泄漏坑。
源码拆完后发现:根本不需要搞什么复杂的全局常驻进程,10 行代码的原生管道就能搞定。”

钩子 2:【实测数据打脸型】

“别再吹某某框架了。这是同一台机器上的真实冷启动实测:
• 方案 A:850ms(常驻内存 120MB)
• 方案 B(自己手搓):19ms(无守护进程,随用随走)
架构图和核心实现压在 Thread 1,自取。”

钩子 3:【独立开发真实心路型】

“一个人写工具第 180 天,最大的感悟是:用户根本不在乎你用了多高级的架构,他们只在乎打开的那一秒卡不卡。”


五、🐙 GitHub / 技术社区:对事不对人的高情商提单与回复

场景 1:给别人提 Issue / PR(自曝先行)

“我们在高并发压力测试(10k QPS)场景下遇到了一个协程泄漏的问题。
刚开始以为是我们自己调用姿势不对,排查了上下文才发现 ConnectionPool 在连接重试时没有显式释放。
复现代码已精简为 15 行脚本,附在下方。如果这个逻辑有其他设计意图,麻烦指正!”

场景 2:维护者回复用户的不合理功能请求(优雅婉拒)

“感谢这个有意思的想法!从使用场景看确实能解决特定边界下的痛点。
不过如果把这个逻辑收进核心库,会破坏当前单二进制零依赖的设计原则,维护成本会显著增加。
建议通过我们提供的插件 hook onBeforeRequest 来实现(示例代码见下方)。暂不收进 core,先 close 保持看板清晰。”

Frequently asked questions

What does the Scenario Openers AI skill do?

「场景接待词、破冰与开场钩子库」分册——彻底剔除AI味,像真人一样在各种具体场合下开口。提供私信接待、客诉安抚、社区Issue回应、网易云神评开场、小红书大情绪钩子、X极客直击钩子等高细粒度原子语料。拒绝空洞升华与机器套话,追求真实的人味、粗粝感与情绪共振。触发:"接待词 / 开场白 / 破冰话术 / 评论区回复 / 去AI味 / 真实人声 / 钩子库 / 怎么开口"。

Why use Scenario Openers on TypingMind?

Because you install it once and use it with any model. Scenario Openers 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 Scenario Openers in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/webkubor/writing-standard-skills/tree/main/skills/scenario-openers. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Scenario Openers?

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 Scenario Openers?

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

Is the Scenario Openers AI skill free?

Yes. It is published on GitHub by webkubor 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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